import keras
import keras.backend as K
from keras.utils.generic_utils import get_from_module
def exp_l1(a, b):
"""Exponential of L1 similarity. Maximum is 1 (a == b), minimum is 0."""
return K.exp(l1(a, b))
def exp_l2(a, b):
"""Exponential of L2 similarity. Maximum is 1 (a == b), minimum is 0."""
return K.exp(l2(a, b))
def l1(a, b):
"""L1 similarity. Maximum is 0 (a == b), minimum is -inf."""
return -K.sum(K.abs(a - b), axis=-1)
def l2(a, b):
"""L2 similarity. Maximum is 0 (a == b), minimum is -inf."""
return -K.sum(K.square(a - b), axis=-1)
def cosine(a, b):
"""Cosine similarity. Maximum is 1 (a == b), minimum is -1 (a == -b)."""
a = K.l2_normalize(a)
b = K.l2_normalize(b)
return 1 - K.mean(a * b, axis=-1)
def sigmoid(a, b):
"""Sigmoid similarity. Maximum is 1 (a == b), minimum is 0."""
return K.sigmoid(K.sum(a * b, axis=-1))
def euclidean(a, b):
"""Euclidian similarity. Maximum is 1 (a == b), minimum is 0 (a == -b)."""
x = K.sum(K.square(a - b), axis=-1)
return 1. / (1. + x)
def geometric(a, b):
"""Geometric mean of sigmoid and euclidian similarity."""
return sigmoid(a, b) * euclidean(a, b)
def arithmetic(a, b):
"""Arithmetic mean of sigmoid and euclidean similarity."""
return (sigmoid(a, b) + euclidean(a, b)) * 0.5
rbf = RBF = exp_l2 # Radial basis function.
gesd = GESD = geometric
aest = AESD = arithmetic
def get(identifier, kwargs=None):
return get_from_module(identifier, globals(), 'similarity',
instantiate=False, kwargs=kwargs)
