uint16 excluded

theano/tensor/nnet/tests/test_sigm.py

@@ -44,7 +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
@@ -54,7 +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
@@ -65,7 +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',

theano/tensor/tests/test_basic.py

@@ -1128,8 +1128,8 @@ _good_broadcast_unary_normal_no_complex = dict(
         normal=[numpy.asarray(rand_ranged(-5, 5, (2, 3)), dtype=floatX)],
         integers=[randint_ranged(-5, 5, (2, 3))],
         int8=[numpy.arange(-127, 128, dtype='int8')],
-        uint8=[numpy.arange(0, 255, dtype='uint8')],
-        uint16=[numpy.arange(0, 65535, dtype='uint16')],
+        uint8=[numpy.arange(0, 89, dtype='uint8')],
+        uint16=[numpy.arange(0, 89, dtype='uint16')],
         corner_case=[corner_case],
         empty=[numpy.asarray([], dtype=config.floatX)],
         )