def test_unweighted(self):
num_data = 10
feat_dim = 6
margin = 1.0
num_classes = 4
embedding = np.random.rand(num_data, feat_dim).astype(np.float32)
labels = np.random.randint(0, num_classes, size=(num_data))
# Reshape labels to compute adjacency matrix.
# TODO: https://github.com/PyCQA/pylint/issues/3139
# pylint: disable=E1136
labels_reshaped = np.reshape(labels.astype(np.float32),
(labels.shape[0], 1))
# pylint: enable=E1136
# Compute the loss in NP.
adjacency = np.equal(labels_reshaped, labels_reshaped.T)
pdist_matrix = pairwise_distance_np(embedding, squared=True)
loss_np = 0.0
num_positives = 0.0
for i in range(num_data):
for j in range(num_data):
if adjacency[i][j] > 0.0 and i != j:
num_positives += 1.0
pos_distance = pdist_matrix[i][j]
neg_distances = []
for k in range(num_data):
if adjacency[i][k] == 0:
neg_distances.append(pdist_matrix[i][k])
# Sort by distance.
neg_distances.sort()
chosen_neg_distance = neg_distances[0]
for l in range(len(neg_distances)):
chosen_neg_distance = neg_distances[l]
if chosen_neg_distance > pos_distance:
break
loss_np += np.maximum(
0.0, margin - chosen_neg_distance + pos_distance)
loss_np /= num_positives
# Compute the loss in TF.
y_true = tf.constant(labels)
y_pred = tf.constant(embedding)
cce_obj = triplet.TripletSemiHardLoss()
loss = cce_obj(y_true, y_pred)
self.assertAlmostEqual(self.evaluate(loss), loss_np, 3)
def test_semihard_tripled_loss_angular(dtype, dist_func, dist_metric):
num_data = 10
feat_dim = 6
margin = 1.0
num_classes = 4
embedding = np.random.rand(num_data, feat_dim).astype(np.float32)
labels = np.random.randint(0, num_classes, size=(num_data))
# Compute the loss in NP.
loss_np = triplet_semihard_loss_np(labels, embedding, margin, dist_func)
# Compute the loss in TF.
y_true = tf.constant(labels)
y_pred = tf.constant(embedding, dtype=dtype)
cce_obj = triplet.TripletSemiHardLoss(distance_metric=dist_metric)
loss = cce_obj(y_true, y_pred)
test_utils.assert_allclose_according_to_type(loss.numpy(), loss_np)
def test_serialization_semihard():
loss = triplet.TripletSemiHardLoss()
tf.keras.losses.deserialize(tf.keras.losses.serialize(loss))
def test_serialization(self):
loss = triplet.TripletSemiHardLoss()
new_loss = tf.keras.losses.deserialize(tf.keras.losses.serialize(loss))
