def test_polynomial_piecewise(self):
tf.reset_default_graph()
prot = ABY3()
tfe.set_protocol(prot)
x = tfe.define_private_variable(tf.constant([[-1, -0.5, -0.25], [0, 0.25, 2]]))
# This is the approximation of the sigmoid function by using a piecewise function:
# f(x) = (0 if x<-0.5), (x+0.5 if -0.5<=x<0.5), (1 if x>=0.5)
z1 = tfe.polynomial_piecewise(
x,
(-0.5, 0.5),
((0,), (0.5, 1), (1,)), # use tuple because list is not hashable
)
# Or, simply use the pre-defined sigmoid API which includes a different approximation
z2 = tfe.sigmoid(x)
with tfe.Session() as sess:
# initialize variables
sess.run(tfe.global_variables_initializer())
# reveal result
result = sess.run(z1.reveal())
np.testing.assert_allclose(
result, np.array([[0, 0, 0.25], [0.5, 0.75, 1]]), rtol=0.0, atol=0.01
)
result = sess.run(z2.reveal())
np.testing.assert_allclose(
result,
np.array([[0.33, 0.415, 0.4575], [0.5, 0.5425, 0.84]]),
rtol=0.0,
atol=0.01,
)
def test_polynomial_piecewise():
tf.reset_default_graph()
import time
start = time.time()
prot = ABY3()
tfe.set_protocol(prot)
x = tfe.define_private_variable(
tf.constant([[-1, -0.5, -0.25], [0, 0.25, 2]]))
# This is the approximation of the sigmoid function by using a piecewise function:
# f(x) = (0 if x<-0.5), (x+0.5 if -0.5<=x<0.5), (1 if x>=0.5)
z1 = tfe.polynomial_piecewise(
x,
(-0.5, 0.5),
(
(0, ), (0.5, 1), (1, )
) # Should use tuple because list is not hashable for the memoir cache key
)
# Or, simply use the pre-defined sigmoid API which includes a different approximation
z2 = tfe.sigmoid(x)
with tfe.Session() as sess:
# initialize variables
sess.run(tfe.global_variables_initializer())
# reveal result
result = sess.run(z1.reveal())
close(result, np.array([[0, 0, 0.25], [0.5, 0.75, 1]]))
result = sess.run(z2.reveal())
close(result, np.array([[0.33, 0.415, 0.4575], [0.5, 0.5425, 0.84]]))
print("test_polynomial_piecewise succeeds")
end = time.time()
print("Elapsed time: {} seconds".format(end - start))