def contrastive_loss_over_distance(labels, distances):
'''
:param labels: 1D tensor containing 0 or 1 for each example
:param distances:
:return:
'''
margin = 1
# loss = K.mean((distances + K.maximum(margin-shifted_distances, 0)))
print(K.eval(distances))
right = margin - distances
print(K.eval(right))
right = K.maximum(right, 0)
print(K.eval(right))
right = K.square(right)
print(K.eval(right))
print ""
print(K.eval(distances))
left = distances
print(K.eval(left))
left = K.square(left)
print(K.eval(left))
left = labels * left
print(K.eval(left))
right = (1 - labels) * right
print(K.eval(right))
loss = K.mean(left + right)
print(K.eval(loss))
# loss = K.mean(distances - shifted_distances)
return loss
def contrastive_test():
# batch size = 3, space dim = 2
t1 = K.variable(
np.array([[.1, .9], [.1, .9], [.2, .8], [.2, .8], [.3, .7], [.3, .7]]))
t2 = K.variable(
np.array([[.0, .0], [.3, .0], [.3, .5], [.9, .5], [.4, .9], [.7, .6]]))
t3 = K.variable(
np.array([[.1, .9], [.3, .0], [.2, .8], [.9, .5], [.3, .7], [.7, .6]]))
t4 = K.variable(
np.array([[.0, .0], [.0, .0], [.0, .0], [.0, .0], [.0, .0], [.0, .0]]))
contr_label = np.array([1, 0, 1, 0, 1, 0])
contr_label = K.variable(contr_label)
d1 = my_distance([t1, t2])
d2 = my_distance([t1, t3])
d3 = my_distance([t1, t4])
l1 = contrastive_loss_over_distance(contr_label, d1)
l1 = K.eval(l1)
l2 = contrastive_loss_over_distance(contr_label, d2)
l2 = K.eval(l2)
l3 = contrastive_loss_over_distance(contr_label, d3)
l3 = K.eval(l3)
print 'loss 1: ' + str(l1)
print 'loss 2: ' + str(l2)
print 'loss 3: ' + str(l3)
def distance_test():
t1 = K.variable(np.array([[1, 0], [1, 0]]))
t2 = K.variable(np.array([[0, 1], [0, 0]]))
#t1 = K.variable(np.array([1, 0]))
#t2 = K.variable(np.array([0, 1]))
d = my_distance([t1, t2])
d = K.eval(d)
print 'distance: ' + str(d)
def cos_distance_test():
# t1 = K.variable(np.array([[1, 1], [1, 1]]))
# t2 = K.variable(np.array([[1, 1], [1, 1]]))
t1 = K.variable(np.array([1, 0]))
t2 = K.variable(np.array([0, 1]))
d = 1 + cosine_proximity(t1, t2)
d = K.eval(d)
print 'Cos distance: ' + str(d)
def test_contrastive_loss_over_distances():
t1 = K.variable(np.array([[0.1, 0.2, 0.9, 0.8], [0.9, 0.95, 0.2, 0.4]]))
t2 = K.variable(
np.array([[0.12, 0.21, 0.89, 0.78], [0.12, 0.21, 0.89, 0.78]]))
d = my_distance([t1, t2])
print 'distance: ' + str(K.eval(d))
labels = np.array([0, 0])
labels_2 = np.array([1, 1])
labels_3 = np.array([1, 0])
labels_4 = np.array([0, 1])
#loss = contrastive_loss_over_distance(labels, d)
#loss2 = contrastive_loss_over_distance(labels_2, d)
loss3 = contrastive_loss_over_distance(labels_3, d)
loss4 = contrastive_loss_over_distance(labels_4, d)
# print "loss: " + str(K.eval(loss))
# print "loss2: " + str(K.eval(loss2))
print "loss3: " + str(K.eval(loss3))
print "loss4: " + str(K.eval(loss4))
