Fix the gradient tests properly by ensuring the input matrices are

theano/tensor/tests/test_nlinalg.py

@@ -403,9 +403,9 @@ class test_Eig(utt.InferShapeTester):
         super(test_Eig, self).setUp()
         self.rng = numpy.random.RandomState(utt.fetch_seed())
         self.A = theano.tensor.matrix(dtype=self.dtype)
-        X = numpy.asarray(self.rng.rand(5, 5),
+        self.X = numpy.asarray(self.rng.rand(5, 5),
                           dtype=self.dtype)
-        self.S = X.dot(X.T)
+        self.S = self.X.dot(self.X.T)
 
     def test_infer_shape(self):
         A = self.A
@@ -439,21 +439,19 @@ class test_Eigh(test_Eig):
                                   vl * numpy.sign(vl[0, :]))
 
     def test_grad(self):
-        S = self.S
-        utt.verify_grad(lambda x: self.op(x)[0], [S], rng=self.rng)
-        utt.verify_grad(lambda x: self.op(x)[1], [S], rng=self.rng, eps=1e-4)
-        utt.verify_grad(lambda x: self.op(x, 'U')[0], [S], rng=self.rng)
-        utt.verify_grad(lambda x: self.op(x, 'U')[1], [S], rng=self.rng, eps=1e-4)
+        X = self.X
+        utt.verify_grad(lambda x: self.op(x.dot(x.T))[0], [X], rng=self.rng)
+        utt.verify_grad(lambda x: self.op(x.dot(x.T))[1], [X], rng=self.rng)
+        utt.verify_grad(lambda x: self.op(x.dot(x.T), 'U')[0], [X], rng=self.rng)
+        utt.verify_grad(lambda x: self.op(x.dot(x.T), 'U')[1], [X], rng=self.rng)
 
 
 class test_Eigh_float32(test_Eigh):
     dtype = 'float32'
 
-    @utt.AttemptManyTimes(n_attempts=3, n_req_successes=2)
     def test_uplo(self):
         super(test_Eigh_float32, self).test_uplo()
 
-    @utt.AttemptManyTimes(n_attempts=3, n_req_successes=2)
     def test_grad(self):
         super(test_Eigh_float32, self).test_grad()
 

theano/tensor/tests/test_slinalg.py

@@ -15,16 +15,9 @@ from theano.tensor.basic import _allclose
 from theano.tests.test_rop import break_op
 from theano.tests import unittest_tools as utt
 from theano import config
-from theano.tensor.slinalg import ( Cholesky,
-                                    cholesky,
-                                    CholeskyGrad,
-                                    Solve,
-                                    solve,
-                                    Eigvalsh,
-                                    EigvalshGrad,
-                                    eigvalsh,
-                                    expm,
-                                    kron)
+from theano.tensor.slinalg import (
+    Cholesky, cholesky, CholeskyGrad, Solve, solve,
+    Eigvalsh, EigvalshGrad, eigvalsh, expm, kron)
 from theano.tests.unittest_tools import attr
 
 from nose.plugins.skip import SkipTest
@@ -80,19 +73,16 @@ def test_cholesky_grad():
     if not imported_scipy:
         raise SkipTest("Scipy needed for the Cholesky op.")
     rng = numpy.random.RandomState(utt.fetch_seed())
-    r = rng.rand(5, 5).astype(config.floatX)
-    pd = numpy.dot(r, r.T)
-    eps = None
-    if config.floatX == "float64":
-        eps = 2e-8
+    r = rng.randn(5, 5).astype(config.floatX)
     # Check the default.
-    yield (lambda: utt.verify_grad(cholesky, [pd], 3, rng, eps=eps))
+    yield (lambda: utt.verify_grad(lambda r: cholesky(r.dot(r.T)),
+                                   [r], 3, rng))
     # Explicit lower-triangular.
-    yield (lambda: utt.verify_grad(Cholesky(lower=True), [pd], 3,
-                                   rng, eps=eps))
+    yield (lambda: utt.verify_grad(lambda r: Cholesky(lower=True)(r.dot(r.T)),
+                                   [r], 3, rng))
     # Explicit upper-triangular.
-    yield (lambda: utt.verify_grad(Cholesky(lower=False), [pd], 3,
-                                   rng, eps=eps))
+    yield (lambda: utt.verify_grad(lambda r: Cholesky(lower=False)(r.dot(r.T)),
+                                   [r], 3, rng))
 
 
 @attr('slow')