More info in tests error

theano/sandbox/cuda/tests/test_mlp.py

@@ -225,7 +225,7 @@ def test_conv_nnet1():
     rval_cpu = run_conv_nnet1(False)
     utt.seed_rng()
     rval_gpu = run_conv_nnet1(True)
-    assert numpy.allclose(rval_cpu, rval_gpu, rtol=1e-4, atol=1e-6)
+    utt.assert_allclose(rval_cpu, rval_gpu, rtol=1e-4, atol=1e-6)
 
 
 def run_conv_nnet2(use_gpu):  # pretend we are training LeNet for MNIST
@@ -318,7 +318,7 @@ def test_conv_nnet2():
         utt.seed_rng()
         rval_cpu = run_conv_nnet2(False)
         # print rval_cpu[0], rval_gpu[0],rval_cpu[0]-rval_gpu[0]
-        assert numpy.allclose(rval_cpu, rval_gpu, rtol=1e-4, atol=1e-4)
+        utt.assert_allclose(rval_cpu, rval_gpu, rtol=1e-4, atol=1e-4)
 
 
 def build_conv_nnet2_classif(use_gpu, isize, ksize, n_batch,
@@ -559,8 +559,8 @@ def cmp_run_conv_nnet2_classif(seed, isize, ksize, bsize,
                     rval_gpu - rval_cpu) / rval_gpu))
 
             if not ignore_error:
-                assert numpy.allclose(rval_cpu, rval_gpu,
-                     rtol=1e-5, atol=float_atol)
+                utt.assert_allclose(rval_cpu, rval_gpu,
+                                    rtol=1e-5, atol=float_atol)
 
             # Synchronize parameters to start from the same point next time
             if i < n_train - 1:

theano/sandbox/tests/test_multinomial.py

@@ -7,6 +7,7 @@ from theano import config, function, tensor
 from theano.sandbox import multinomial
 from theano.compile.mode import get_default_mode, predefined_linkers
 import theano.sandbox.cuda as cuda
+import theano.tests.unittest_tools as utt
 
 
 def get_mode(gpu):
@@ -45,22 +46,22 @@ def test_multinomial_0():
                         for node in f.maker.fgraph.toposort()])
 
         # test that both first and second samples can be drawn
-        assert numpy.allclose(f([[1, 0], [0, 1]], [.1, .1]),
+        utt.assert_allclose(f([[1, 0], [0, 1]], [.1, .1]),
                               [[2, 0], [0, 2]])
 
         # test that both second labels can be drawn
         r = f([[.2, .8], [.3, .7]], [.31, .31])
-        assert numpy.allclose(r, [[0, 2], [0, 2]]), r
+        utt.assert_allclose(r, [[0, 2], [0, 2]])
 
         # test that both first labels can be drawn
         r = f([[.2, .8], [.3, .7]], [.21, .21])
-        assert numpy.allclose(r, [[0, 2], [2, 0]]), r
+        utt.assert_allclose(r, [[0, 2], [2, 0]])
 
         # change the size to make sure output gets reallocated ok
         # and also make sure that the GPU version doesn't screw up the
         # transposed-ness
         r = f([[.2, .8]], [.25])
-        assert numpy.allclose(r, [[0, 2]]), r
+        utt.assert_allclose(r, [[0, 2]])
 
     run_with_c(body)
     if cuda.cuda_available:
@@ -93,9 +94,9 @@ def test_multinomial_large():
             assert mval.dtype == 'float64'
         else:
             raise NotImplementedError(config.cast_policy)
-        assert numpy.allclose(mval.sum(axis=1), 2)
+        utt.assert_allclose(mval.sum(axis=1), 2)
         asdf = numpy.asarray([0, 0, 2, 0])+0*pval
-        assert numpy.allclose(mval, asdf)  # broadcast over all rows
+        utt.assert_allclose(mval, asdf)  # broadcast over all rows
     run_with_c(body)
     if cuda.cuda_available:
         run_with_c(body, True)