def testLiftedStruct(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)).astype(np.float32)
# Reshape labels to compute adjacency matrix.
# pylint: disable=E1136
labels_reshaped = np.reshape(labels, (labels.shape[0], 1))
# pylint: enable=E1136
# Compute the loss in NP
# pylint: disable=E1111
adjacency = np.equal(labels_reshaped, labels_reshaped.T)
# pylint: enable=E1111
pdist_matrix = pairwise_distance_np(embedding)
loss_np = 0.0
num_constraints = 0.0
for i in range(num_data):
for j in range(num_data):
if adjacency[i][j] > 0.0 and i != j:
d_pos = pdist_matrix[i][j]
negs = []
for k in range(num_data):
if not adjacency[i][k]:
negs.append(margin - pdist_matrix[i][k])
for l in range(num_data):
if not adjacency[j][l]:
negs.append(margin - pdist_matrix[j][l])
negs = np.array(negs)
max_elem = np.max(negs)
negs -= max_elem
negs = np.exp(negs)
soft_maximum = np.log(np.sum(negs)) + max_elem
num_constraints += 1.0
this_loss = max(soft_maximum + d_pos, 0)
loss_np += this_loss * this_loss
loss_np = loss_np / num_constraints / 2.0
# Compute the loss in TF.
y_true = tf.constant(labels)
y_pred = tf.constant(embedding)
cce_obj = lifted.LiftedStructLoss()
loss = cce_obj(y_true, y_pred)
self.assertAlmostEqual(self.evaluate(loss), loss_np, 3)
def test_lifted_struct(dtype):
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).astype(np.float32)
# Compute the loss in NP
loss_np = lifted_struct_loss_np(labels, embedding, margin)
# Compute the loss in TF.
y_true = tf.constant(labels)
y_pred = tf.constant(embedding, dtype=dtype)
cce_obj = lifted.LiftedStructLoss()
loss = cce_obj(y_true, y_pred)
test_utils.assert_allclose_according_to_type(loss.numpy(), loss_np)
def test_serialization(self):
loss = lifted.LiftedStructLoss()
new_loss = tf.keras.losses.deserialize(tf.keras.losses.serialize(loss))
def test_serialization():
loss = lifted.LiftedStructLoss()
tf.keras.losses.deserialize(tf.keras.losses.serialize(loss))