Merge pull request #2380 from nouiz/tests

theano/sandbox/cuda/tests/test_mlp.py

@@ -397,7 +397,9 @@ def build_conv_nnet2_classif(use_gpu, isize, ksize, n_batch,
         # Check that GpuConv is used
         topo = train.maker.fgraph.toposort()
         conv_ops = (tcn.blas.GpuConv,
-                    tcn.dnn.GpuDnnConvBase,
+                    tcn.dnn.GpuDnnConv,
+                    tcn.dnn.GpuDnnConvGradI,
+                    tcn.dnn.GpuDnnConvGradW,
                     tcn.blas.BaseGpuCorrMM)
 
         assert len([n for n in topo if isinstance(n.op, conv_ops)]) > 0

theano/sandbox/rng_mrg.py

@@ -1007,7 +1007,7 @@ class GPUA_mrg_uniform(GpuKernelBase, mrg_uniform_base):
         return (3, self.GpuKernelBase_version)
 
 
-def guess_n_streams(size, warn=True):
+def guess_n_streams(size, warn=False):
     """
     Return a guess at a good number of streams.
 
@@ -1128,7 +1128,7 @@ class MRG_RandomStreams(object):
         return rval
 
     def n_streams(self, size):
-        return guess_n_streams(size, warn=True)
+        return guess_n_streams(size)
 
     def pretty_return(self, node_rstate, new_rstate, sample):
         sample.rstate = node_rstate

theano/tensor/tests/test_slinalg.py

@@ -251,10 +251,11 @@ def test_expm_grad_1():
     if not imported_scipy:
         raise SkipTest("Scipy needed for the expm op.")
     rng = numpy.random.RandomState(utt.fetch_seed())
-    A = rng.randn(5, 5).astype(config.floatX)
+    # Always test in float64 for better numerical stability.
+    A = rng.randn(5, 5)
     A = A + A.T
 
-    tensor.verify_grad(expm, [A,], rng=rng)
+    tensor.verify_grad(expm, [A], rng=rng)
 
 
 def test_expm_grad_2():
@@ -262,12 +263,13 @@ def test_expm_grad_2():
     if not imported_scipy:
         raise SkipTest("Scipy needed for the expm op.")
     rng = numpy.random.RandomState(utt.fetch_seed())
-    A = rng.randn(5, 5).astype(config.floatX)
-    w = (rng.randn(5).astype(config.floatX))**2
+    # Always test in float64 for better numerical stability.
+    A = rng.randn(5, 5)
+    w = rng.randn(5)**2
     A = (numpy.diag(w**0.5)).dot(A + A.T).dot(numpy.diag(w**(-0.5)))
     assert not numpy.allclose(A, A.T)
 
-    tensor.verify_grad(expm, [A,], rng=rng)
+    tensor.verify_grad(expm, [A], rng=rng)
 
 
 def test_expm_grad_3():
@@ -275,6 +277,7 @@ def test_expm_grad_3():
     if not imported_scipy:
         raise SkipTest("Scipy needed for the expm op.")
     rng = numpy.random.RandomState(utt.fetch_seed())
-    A = rng.randn(5, 5).astype(config.floatX)
+    # Always test in float64 for better numerical stability.
+    A = rng.randn(5, 5)
 
-    tensor.verify_grad(expm, [A,], rng=rng)
+    tensor.verify_grad(expm, [A], rng=rng)