Disable grad/lop/rop/verify_grad on complex.

theano/gradient.py

@@ -132,6 +132,13 @@ def grad_sources_inputs(sources, graph_inputs, warn_type=True):
         #if all output gradients are None, continue
         if all(map(lambda x: x is None, g_outputs)): continue
 
+        #Disable all grad operation on complex. verify_grad don't
+        #support them and we don't know we want to handle them.
+        for var in node.inputs + node.outputs:
+            if (hasattr(var.type, 'dtype') and "complex" in var.type.dtype):
+                raise Exception("We do not support grad/Rop/Lop/verify_grad"
+                                " on complex.")
+
         output_arg = g_outputs
         input_arg = node.inputs
 

theano/tensor/tests/test_complex.py

@@ -46,6 +46,7 @@ class TestRealImag(unittest.TestCase):
         assert numpy.all(rval == mval[0]), (rval,mval[0])
         assert numpy.all(ival == mval[1]), (ival, mval[1])
 
+    @dec.knownfailureif(True,"Complex grads not enabled, see #178")
     def test_complex_grads(self):
         def f(m):
             c = complex(m[0], m[1])
@@ -103,6 +104,7 @@ class TestRealImag(unittest.TestCase):
             print e.analytic_grad
             raise
 
+    @dec.knownfailureif(True,"Complex grads not enabled, see #178")
     def test_polar_grads(self):
         def f(m):
             c = complex_from_polar(abs(m[0]), m[1])
@@ -112,6 +114,7 @@ class TestRealImag(unittest.TestCase):
         mval = numpy.asarray(rng.randn(2,5))
         utt.verify_grad(f, [mval])
 
+    @dec.knownfailureif(True,"Complex grads not enabled, see #178")
     def test_abs_grad(self):
         def f(m):
             c = complex(m[0], m[1])

theano/tensor/tests/test_elemwise.py

@@ -628,6 +628,8 @@ class T_sum_dtype(unittest.TestCase):
                     sum_var = x.sum(dtype=output_dtype, axis=axis)
                     assert sum_var.dtype == output_dtype
 
+                    if "complex" in input_dtype:
+                        continue
                     # Check that we can take the gradient
                     grad_var = tensor.grad(sum_var.sum(), x,
                             disconnected_inputs='ignore')
@@ -676,6 +678,8 @@ class T_mean_dtype(unittest.TestCase):
                         assert mean_var.dtype == sum_dtype, (mean_var.dtype, sum_dtype)
 
                     # Check that we can take the gradient, when implemented
+                    if "complex" in mean_var.dtype:
+                        continue
                     try:
                         grad_var = tensor.grad(mean_var.sum(), x,
                                 disconnected_inputs='ignore')
@@ -729,6 +733,8 @@ class T_prod_dtype(unittest.TestCase):
                     prod_var = x.prod(dtype=output_dtype, axis=axis)
                     assert prod_var.dtype == output_dtype
 
+                    if "complex" in output_dtype:
+                        continue
                     # Check that we can take the gradient
                     grad_var = tensor.grad(prod_var.sum(), x,
                             disconnected_inputs='ignore')

theano/tensor/tests/test_fourier.py

 import numpy
+from numpy.testing import dec
+
 import theano
 from theano import tensor
 from theano.tests import unittest_tools as utt
@@ -37,6 +39,7 @@ class TestFourier(utt.InferShapeTester):
                                     [numpy.random.rand(12, 4), 0],
                                     self.op_class)
 
+    @dec.knownfailureif(True, "Complex grads not enabled, see #178")
     def test_gradient(self):
         def fft_test1(a):
             return self.op(a, None, None)