class TestSigmoid:
def setup_method(self):
utt.seed_rng()
def test_elemwise(self):
utt.verify_grad(sigmoid, [np.random.rand(3, 4)])
TestSigmoidBroadcast = makeBroadcastTester(
op=sigmoid,
expected=upcast_int8_nfunc(
lambda inputs: check_floatX(inputs, 1 / (1 + np.exp(-inputs)))
),
good=copymod(
_good_broadcast_unary_normal_no_complex, without=["uint16"]
), # The reason that 'uint16' is excluted is that
# theano works well but numpy overflows resulting
# in an assertion error.
# grad=_grad_broadcast_unary_normal,
name="SigmoidTester",
eps=1e-8,
)
TestUltraFastSigmoidBroadcast = makeBroadcastTester(
op=ultra_fast_sigmoid,
expected=upcast_int8_nfunc(
lambda inputs: check_floatX(inputs, 1 / (1 + np.exp(-inputs)))
),
good=copymod(
_good_broadcast_unary_normal_no_complex, without=["uint16"]
eps=1e-8,
inplace=True,
)
class TestSigmoid:
def setup_method(self):
utt.seed_rng()
def test_elemwise(self):
utt.verify_grad(aet.sigmoid, [np.random.rand(3, 4)])
_good_broadcast_unary_softplus = dict(
copymod(
_good_broadcast_unary_normal_no_complex,
without=["uint8", "uint16", "big_scalar"],
), # numpy function overflows with uint16.
uint8=[np.arange(0, 89, dtype="uint8")
], # the range is different in new added uint8.
int8=[np.arange(-127, 89, dtype="int8")],
)
expected_sofplus = upcast_int8_nfunc(
lambda inputs: check_floatX(inputs, np.log1p(np.exp(inputs))))
TestSoftplusBroadcast = makeBroadcastTester(
op=aet.softplus,
expected=expected_sofplus,
good=_good_broadcast_unary_softplus,
eps=1e-8,
)
TestMulInplaceBroadcast = makeBroadcastTester(
op=mul_inplace,
expected=lambda x, y: x * y,
good=_good_broadcast_binary_normal,
bad_build=_bad_build_broadcast_binary_normal,
bad_runtime=_bad_runtime_broadcast_binary_normal,
inplace=True,
)
TestTrueDivInplaceBroadcast = makeBroadcastTester(
op=true_div_inplace,
expected=_numpy_true_div,
good=copymod(
_good_broadcast_div_mod_normal_float_inplace,
# The output is now in float, we cannot work inplace on an int.
without=["integer", "uint8", "uint16", "int8"],
),
grad_rtol=div_grad_rtol,
inplace=True,
)
TestInvInplaceBroadcast = makeBroadcastTester(
op=inv_inplace,
expected=lambda x: _numpy_true_div(np.int8(1), x),
good=_good_inv_inplace,
bad_runtime=_bad_runtime_inv,
grad_rtol=div_grad_rtol,
inplace=True,
)
from aesara.tensor.math import clip, sigmoid
from aesara.tensor.nnet.sigm import hard_sigmoid, ultra_fast_sigmoid
from aesara.tensor.type import matrix
from tests.tensor.utils import (
_good_broadcast_unary_normal_no_complex,
check_floatX,
copymod,
makeBroadcastTester,
upcast_int8_nfunc,
)
TestUltraFastSigmoidBroadcast = makeBroadcastTester(
op=ultra_fast_sigmoid,
expected=upcast_int8_nfunc(
lambda inputs: check_floatX(inputs, 1 / (1 + np.exp(-inputs)))),
good=copymod(_good_broadcast_unary_normal_no_complex,
without=["uint16"]), # numpy fucnting overflows with uint16.
# grad=_grad_broadcast_unary_normal,
name="UltraFastSigmoidTester",
# This is an approx of the sigmoid. That is why we raise eps
eps=5e-2,
)
TestHardSigmoidBroadcast = makeBroadcastTester(
op=hard_sigmoid,
expected=upcast_int8_nfunc(
lambda inputs: check_floatX(inputs, 1 / (1 + np.exp(-inputs)))),
good=copymod(_good_broadcast_unary_normal_no_complex,
without=["uint16"]), # numpy fucnting overflows with uint16.
# grad=_grad_broadcast_unary_normal,
name="HardSigmoidTester",
# This is an approx of the sigmoid. That is why we raise eps