def test_classification_triplet_loss():
# NOTE: can be slow if compilation not cached
y = np.array([0, 0, 1, 1], dtype=np.int32)
y_true = T.constant(y)
embeddings = theano.shared(np.random.randn(4, 128).astype(fX))
loss = classification_triplet_loss(embeddings, y_true)
grad = T.grad(loss, [embeddings])[0]
# SGD
new_embeddings = (embeddings - 0.01 * grad)
# set embeddings to have norm of 1
new_embeddings2 = (new_embeddings
/ l2_norm(new_embeddings, axis=1, keepdims=True))
fn = theano.function([], [loss], updates={embeddings: new_embeddings2})
prev_loss = np.inf
for _ in range(200):
l = fn()[0]
assert l < prev_loss
import scipy.spatial.distance
vecs = embeddings.get_value()
dist_matrix = scipy.spatial.distance.cdist(vecs, vecs)
for row, same, differents in [(0, 1, (2, 3)),
(1, 0, (2, 3)),
(2, 3, (0, 1)),
(3, 2, (0, 1))]:
for different in differents:
assert dist_matrix[row, same] < dist_matrix[row, different], dict(
dist_matrix=dist_matrix,
row=row,
same=same,
different=different,
)
def test_classification_triplet_same_label():
# NOTE: can be slow if compilation not cached
# test what happens when there are no triplets (all have the same label)
y = np.array([0, 0, 0], dtype=np.int32)
y_true = T.constant(y)
embeddings = theano.shared(np.random.randn(3, 128).astype(fX))
loss = classification_triplet_loss(embeddings, y_true)
loss.eval()
def test_classification_triplet_same_embedding():
# NOTE: can be slow if compilation not cached
# test what happens when all triplets have the same label
y = np.array([0, 1, 0, 1], dtype=np.int32)
y_true = T.constant(y)
embeddings = theano.shared(np.zeros((4, 128), dtype=fX))
loss = classification_triplet_loss(embeddings, y_true)
embeddings_g = T.grad(loss, embeddings)
embeddings_g.eval()