Removed dependency between tensor/basic.py and unittest_tools (which was getting

doc/topics/unittest.txt

@@ -393,6 +393,12 @@ Here is an example showing how to use verify_grad:
 >>>    # ...
 >>>    tensor.verify_grad(Flatten(), [a_val])
 
+.. note::
+
+    Although ``verify_grad`` is defined in ``theano.tensor.basic``, unittests
+    should use the version of ``verify_grad`` defined in ``theano.tests.unittest_tools``.
+    This is simply a wrapper function which takes care of seeding the random 
+    number generator appropriately before calling ``theano.tensor.basic.verify_grad``
 
 makeTester and makeBroadcastTester
 ==================================

theano/sparse/tests/test_basic.py

@@ -11,7 +11,7 @@ from theano import gof
 
 from theano.sparse.basic import _is_dense, _is_sparse, _is_dense_variable, _is_sparse_variable
 from theano.sparse.basic import _mtypes, _mtype_to_str
-from theano.tests import unittest_tools
+from theano.tests import unittest_tools as utt
 
 
 def eval_outputs(outputs):
@@ -19,7 +19,7 @@ def eval_outputs(outputs):
 
 class T_transpose(unittest.TestCase):
     def setUp(self):
-        unittest_tools.seed_rng()
+        utt.seed_rng()
 
     def test_transpose_csc(self):
         sp = sparse.csc_matrix(sparse.eye(5,3))
@@ -126,7 +126,7 @@ class T_Add(unittest.TestCase):
 
 class T_conversion(unittest.TestCase):
     def setUp(self):
-        unittest_tools.seed_rng()
+        utt.seed_rng()
 
     def test0(self):
         a = tensor.as_tensor_variable(numpy.random.rand(5))
@@ -157,7 +157,7 @@ class T_conversion(unittest.TestCase):
 import scipy.sparse as sp
 class test_structureddot(unittest.TestCase):
     def setUp(self):
-        unittest_tools.seed_rng()
+        utt.seed_rng()
 
     def test_structuredot(self):
         bsize = 2
@@ -193,7 +193,7 @@ class test_structureddot(unittest.TestCase):
             assert _is_dense(c)
             assert numpy.all(outvals == c)
 
-            tensor.verify_grad(buildgraphCSC, [kernvals,imvals])
+            utt.verify_grad(buildgraphCSC, [kernvals,imvals])
 
             ##
             # Test compressed-sparse row matrices ###
@@ -215,7 +215,7 @@ class test_structureddot(unittest.TestCase):
             assert _is_dense(c)
             assert numpy.all(outvals == c)
 
-            tensor.verify_grad( buildgraphCSR, [kernvals,imvals])
+            utt.verify_grad( buildgraphCSR, [kernvals,imvals])
 
 
 if __name__ == '__main__':

theano/tensor/basic.py

@@ -20,7 +20,6 @@ from ..gof.python25 import partial
 
 from .. import compile, printing
 from ..printing import pprint, Print
-from ..tests import unittest_tools
 
 ### set up the external interface
 from elemwise import Elemwise, DimShuffle, CAReduce, Sum

theano/tensor/tests/test_nnet.py

@@ -5,86 +5,86 @@ from theano import tensor as T
 from theano import gof
 import test_basic as TT
 import numpy
-from theano.tests import unittest_tools
+from theano.tests import unittest_tools as utt
 
 from theano.tensor.nnet import *
 
 
 class T_sigmoid(unittest.TestCase):
     def setUp(self):
-        unittest_tools.seed_rng()
+        utt.seed_rng()
     def test_elemwise(self):
-        TT.verify_grad(sigmoid, [numpy.random.rand(3,4)])
+        utt.verify_grad(sigmoid, [numpy.random.rand(3,4)])
 
 class T_softplus(unittest.TestCase):
     def setUp(self):
-        unittest_tools.seed_rng()
+        utt.seed_rng()
     def test_elemwise(self):
-        TT.verify_grad(softplus, [numpy.random.rand(3,4)])
+        utt.verify_grad(softplus, [numpy.random.rand(3,4)])
 
 class T_Softmax(unittest.TestCase):
     def setUp(self):
-        unittest_tools.seed_rng()
+        utt.seed_rng()
     def test0(self):
         def f(a):
             return softmax(a)[:,0]
-        TT.verify_grad(f, [numpy.random.rand(3,4)])
+        utt.verify_grad(f, [numpy.random.rand(3,4)])
     def test1(self):
         def f(a):
             return softmax(a)[:,1]
-        TT.verify_grad(f, [numpy.random.rand(3,4)])
+        utt.verify_grad(f, [numpy.random.rand(3,4)])
     def test2(self):
         def f(a):
             return softmax(a)[:,2]
-        TT.verify_grad(f, [numpy.random.rand(3,4)])
+        utt.verify_grad(f, [numpy.random.rand(3,4)])
     def test3(self):
         def f(a):
             return softmax(a)[:,3]
-        TT.verify_grad(f, [numpy.random.rand(3,4)])
+        utt.verify_grad(f, [numpy.random.rand(3,4)])
 
 
 class T_SoftmaxWithBias(unittest.TestCase):
     def setUp(self):
-        unittest_tools.seed_rng()
+        utt.seed_rng()
     def test0(self):
         def f(a, b):
             return softmax_with_bias(a, b)[:,0]
-        TT.verify_grad(f, [numpy.random.rand(3,4),
+        utt.verify_grad(f, [numpy.random.rand(3,4),
             numpy.random.rand(4)])
     def test1(self):
         def f(a, b):
             return softmax_with_bias(a, b)[:,1]
-        TT.verify_grad(f, [numpy.random.rand(3,4),
+        utt.verify_grad(f, [numpy.random.rand(3,4),
             numpy.random.rand(4)])
     def test2(self):
         def f(a, b):
             return softmax_with_bias(a, b)[:,2]
-        TT.verify_grad(f, [numpy.random.rand(3,4),
+        utt.verify_grad(f, [numpy.random.rand(3,4),
             numpy.random.rand(4)])
     def test3(self):
         def f(a, b):
             return softmax_with_bias(a, b)[:,3]
-        TT.verify_grad(f, [numpy.random.rand(3,4),
+        utt.verify_grad(f, [numpy.random.rand(3,4),
             numpy.random.rand(4)])
 
 class T_CrossentropySoftmax1Hot(unittest.TestCase):
     def setUp(self):
-        unittest_tools.seed_rng()
+        utt.seed_rng()
     def test0(self):
         y_idx = [0,1,3]
         def f(a, b):
             return crossentropy_softmax_1hot_with_bias(a, b, y_idx)[0]
-        TT.verify_grad(f, [numpy.random.rand(3,4),
+        utt.verify_grad(f, [numpy.random.rand(3,4),
             numpy.random.rand(4)])
     def test1(self):
         y_idx = [0,1,3]
         def f(a):
             return crossentropy_softmax_1hot(a, y_idx)[0]
-        TT.verify_grad(f, [numpy.random.rand(3,4)])
+        utt.verify_grad(f, [numpy.random.rand(3,4)])
 
 class T_prepend(unittest.TestCase):
     def setUp(self):
-        unittest_tools.seed_rng()
+        utt.seed_rng()
     def test0(self):
         """basic functionality"""
         x=tensor.matrix('x')
@@ -110,7 +110,7 @@ class T_prepend(unittest.TestCase):
 
 class T_solve(unittest.TestCase):
     def setUp(self):
-        self.rng = numpy.random.RandomState(unittest_tools.fetch_seed(666))
+        self.rng = numpy.random.RandomState(utt.fetch_seed(666))
 
     def test0(self):
         A=self.rng.randn(5,5)

theano/tensor/tests/test_xlogx.py

@@ -8,11 +8,11 @@ import test_basic as TT
 
 import random
 import numpy.random
-from theano.tests import unittest_tools
+from theano.tests import unittest_tools as utt
 
 class T_XlogX(unittest.TestCase):
     def setUp(self):
-        unittest_tools.seed_rng()
+        utt.seed_rng()
 
     def test0(self):
         x = as_tensor_variable([1, 0])
@@ -23,7 +23,7 @@ class T_XlogX(unittest.TestCase):
 #        class Dummy(object):
 #            def make_node(self, a):
 #                return [xlogx(a)[:,2]]
-        TT.verify_grad(xlogx, [numpy.random.rand(3,4)])
+        utt.verify_grad(xlogx, [numpy.random.rand(3,4)])
 
 
 if __name__ == '__main__':

theano/tests/unittest_tools.py

 import unittest
 import numpy
+import theano.tensor as T
 
 import os, sys
 
@@ -40,3 +41,14 @@ def seed_rng(pseed=None):
                 'instead of seed %i given as parameter' % (seed, pseed)
     numpy.random.seed(seed)
     return seed
+
+def verify_grad(op, pt, n_tests=2, rng=None, eps=1.0e-7, tol=0.0001):
+    """
+    Wrapper for tensor/basic.py:verify_grad
+    Takes care of seeding the random number generator if None is given
+    """
+    if rng is None:
+        seed_rng()
+        rng = numpy.random
+    T.verify_grad(op, pt, n_tests, rng, eps, tol)
+