Esempio n. 1
0
from theano.tensor.tests.test_basic import (makeBroadcastTester, rand,
                                            check_floatX, upcast_int8_nfunc,
                                            _good_broadcast_unary_normal_no_complex)


class T_sigmoid(unittest.TestCase):
    def setUp(self):
        utt.seed_rng()

    def test_elemwise(self):
        utt.verify_grad(sigmoid, [numpy.random.rand(3, 4)])


SigmoidTester = makeBroadcastTester(
    op=sigmoid,
    expected=upcast_int8_nfunc(lambda inputs: check_floatX(
        inputs, 1 / (1 + numpy.exp(-inputs)))),
    good=_good_broadcast_unary_normal_no_complex,
    # grad=_grad_broadcast_unary_normal,
    name='SigmoidTester',
)

UltraFastSigmoidTester = makeBroadcastTester(
    op=ultra_fast_sigmoid,
    expected=upcast_int8_nfunc(lambda inputs: check_floatX(
        inputs, 1 / (1 + numpy.exp(-inputs)))),
    good=_good_broadcast_unary_normal_no_complex,
    # grad=_grad_broadcast_unary_normal,
    name='UltraFastSigmoidTester',
# This is an approx of the sigmoid. That is why we raise eps
    eps=5e-2)
Esempio n. 2
0
    makeBroadcastTester, copymod, check_floatX, upcast_int8_nfunc,
    _good_broadcast_unary_normal_no_complex)


class T_sigmoid(unittest.TestCase):
    def setUp(self):
        utt.seed_rng()

    def test_elemwise(self):
        utt.verify_grad(sigmoid, [np.random.rand(3, 4)])


SigmoidTester = 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',
)

UltraFastSigmoidTester = 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.
Esempio n. 3
0
from theano.tensor.tests.test_basic import (makeBroadcastTester, rand,
                                            check_floatX,
                                            _good_broadcast_unary_normal_no_complex)


class T_sigmoid(unittest.TestCase):
    def setUp(self):
        utt.seed_rng()

    def test_elemwise(self):
        utt.verify_grad(sigmoid, [numpy.random.rand(3, 4)])


SigmoidTester = makeBroadcastTester(
    op=sigmoid,
    expected=lambda inputs: check_floatX(
        inputs, 1/(1+numpy.exp(-inputs))),
    good=_good_broadcast_unary_normal_no_complex,
    #grad=_grad_broadcast_unary_normal,
    name='SigmoidTester',
)

UltraFastSigmoidTester = makeBroadcastTester(
    op=ultra_fast_sigmoid,
    expected=lambda inputs: check_floatX(
        inputs, 1/(1+numpy.exp(-inputs))),
    good=_good_broadcast_unary_normal_no_complex,
    #grad=_grad_broadcast_unary_normal,
    name='UltraFastSigmoidTester',
# This is an approx of the sigmoid. That is why we raise eps
    eps=5e-2)