Merge pull request #5182 from mohammadpz/add_uint_test

theano/scalar/basic.py

@@ -854,6 +854,14 @@ def upgrade_to_float(*types):
                                            for type in types])),
 
 
+def upgrade_to_float64(*types):
+    """
+    Upgrade any int and float32 to float64 to do as SciPy.
+
+    """
+    return get_scalar_type('float64'),
+
+
 def same_out(type):
     return type,
 

theano/scalar/basic_scipy.py

@@ -6,6 +6,7 @@ import numpy
 import theano
 from theano.scalar.basic import (UnaryScalarOp, BinaryScalarOp,
                                  exp, upgrade_to_float,
+                                 upgrade_to_float64,
                                  float_types)
 from theano.scalar.basic import (upgrade_to_float_no_complex,
                                  complex_types, discrete_types,
@@ -369,7 +370,7 @@ class Chi2SF(BinaryScalarOp):
             return Chi2SF.st_impl(x, k)
         else:
             super(Chi2SF, self).impl(x, k)
-chi2sf = Chi2SF(upgrade_to_float, name='chi2sf')
+chi2sf = Chi2SF(upgrade_to_float64, name='chi2sf')
 
 
 class J1(UnaryScalarOp):

theano/tensor/nnet/tests/test_sigm.py

@@ -16,7 +16,7 @@ from theano.tensor.nnet.sigm import (
     compute_mul, is_1pexp, parse_mul_tree, perform_sigm_times_exp,
     register_local_1msigmoid, simplify_mul,
 )
-from theano.tensor.tests.test_basic import (makeBroadcastTester,
+from theano.tensor.tests.test_basic import (makeBroadcastTester, copymod,
                                             check_floatX, upcast_int8_nfunc,
                                             _good_broadcast_unary_normal_no_complex)
 
@@ -28,12 +28,14 @@ class T_sigmoid(unittest.TestCase):
     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,
+    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',
 )
@@ -42,7 +44,8 @@ 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,
+    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
@@ -52,7 +55,8 @@ HardSigmoidTester = makeBroadcastTester(
     op=hard_sigmoid,
     expected=upcast_int8_nfunc(lambda inputs: check_floatX(
         inputs, 1 / (1 + numpy.exp(-inputs)))),
-    good=_good_broadcast_unary_normal_no_complex,
+    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
@@ -63,7 +67,9 @@ SoftplusTester = makeBroadcastTester(
     op=softplus,
     expected=upcast_int8_nfunc(lambda inputs: check_floatX(
         inputs, numpy.log1p(numpy.exp(inputs)))),
-    good=dict(_good_broadcast_unary_normal_no_complex,
+    good=dict(copymod(_good_broadcast_unary_normal_no_complex,
+                      without=['uint8', 'uint16']),  # numpy fucnting overflows with uint16.
+              uint8=[numpy.arange(0, 89, dtype='uint8')],  # the range is different in new added uint8.
               int8=[numpy.arange(-127, 89, dtype='int8')]),
     # grad=_grad_broadcast_unary_normal,
     name='SoftplusTester',