def test_save_model(tmpdir):
model = tf.keras.models.Sequential([
tf.keras.layers.Input((256, 256, 3)),
tf.keras.layers.Dense(6, activation="softmax"),
])
model.compile(optimizer="adam", loss=WeightedKappaLoss(num_classes=6))
model.save(str(tmpdir / "test.h5"))
def test_linear_weighted_kappa_loss(np_seed):
y_true, y_pred = gen_labels_and_preds(50, 4, np_seed)
kappa_loss = WeightedKappaLoss(num_classes=4, weightage="linear")
y_pred = y_pred.astype(np.float32)
loss = kappa_loss(y_true, y_pred)
loss_np = weighted_kappa_loss_np(y_true, y_pred, weightage="linear")
np.testing.assert_allclose(loss, loss_np, rtol=1e-5, atol=1e-5)
def test_quadratic_weighted_kappa_loss(np_seed):
y_true, y_pred = gen_labels_and_preds(100, 3, np_seed)
kappa_loss = WeightedKappaLoss(num_classes=3)
y_pred = y_pred.astype(np.float32)
loss = kappa_loss(y_true, y_pred)
loss_np = weighted_kappa_loss_np(y_true, y_pred)
np.testing.assert_allclose(loss, loss_np, rtol=1e-5, atol=1e-5)
def test_config():
kappa_loss = WeightedKappaLoss(num_classes=4,
weightage="linear",
name="kappa_loss",
epsilon=0.001)
assert kappa_loss.num_classes == 4
assert kappa_loss.weightage == "linear"
assert kappa_loss.name == "kappa_loss"
np.testing.assert_allclose(kappa_loss.epsilon, 0.001, 1e-6)
def test_serialization():
loss = WeightedKappaLoss(num_classes=3)
tf.keras.losses.deserialize(tf.keras.losses.serialize(loss))