random fixes

theano/gpuarray/sort.py

@@ -259,10 +259,10 @@ class GpuTopKOp(GpuKernelBase, TopKOp):
         '''
         return code % locals()
 
-    def make_node(self, inp, k):
+    def make_node(self, inp, kth):
         ctx_name = infer_context_name(inp)
         inp = as_gpuarray_variable(inp, ctx_name)
-        k = as_tensor_variable(k)
+        kth = as_tensor_variable(kth)
         bcast = inp.type.broadcastable
         outs = []
         if self.return_values:
@@ -272,7 +272,7 @@ class GpuTopKOp(GpuKernelBase, TopKOp):
                 dtype=self.idx_dtype,
                 broadcastable=bcast,
                 context_name=ctx_name)())
-        return Apply(self, [inp, k], outs)
+        return Apply(self, [inp, kth], outs)
 
     def get_params(self, node):
         return node.inputs[0].type.context

theano/tensor/sort.py

@@ -357,7 +357,7 @@ class TopKOp(theano.Op):
         return '%(op)s{axis=%(axis)d}' % dict(
             op=self.__class__.__name__, axis=self.axis)
 
-    def make_node(self, inp, k):
+    def make_node(self, inp, kth):
         # numpy always uses int64 as output dtype for arg*() routines
         # however, we add this option as memory is more precious on gpu
         inp = theano.tensor.as_tensor_variable(inp)
@@ -369,7 +369,7 @@ class TopKOp(theano.Op):
                 '"axis" parameter out of range,'
                 ' expected integer within [%d, %d]' % (-ndim, ndim - 1))
 
-        k = theano.tensor.as_tensor_variable(k)
+        kth = theano.tensor.as_tensor_variable(kth)
         bcast = inp.type.broadcastable
         outs = []
         if self.return_values:
@@ -377,7 +377,7 @@ class TopKOp(theano.Op):
         if self.return_indices:
             outs.append(theano.tensor.TensorType(
                 dtype=self.idx_dtype, broadcastable=bcast)())
-        return theano.Apply(self, [inp, k], outs)
+        return theano.Apply(self, [inp, kth], outs)
 
     def perform(self, node, inputs, output_storage):
         x, k = inputs
@@ -428,7 +428,7 @@ class TopKOp(theano.Op):
         return [x_grad, k_grad]
 
 
-def topk(x, k, axis=-1, idx_dtype='int64'):
+def topk(x, kth, axis=-1, idx_dtype='int64'):
     """
     Returns the k-largest elements along an axis.
 
@@ -437,7 +437,7 @@ def topk(x, k, axis=-1, idx_dtype='int64'):
 
     x: tensor instance
 
-    k: integer constant/variable
+    kth: integer constant/variable
         Must not be 0. If negative, gives k-smallest elements instead.
 
     axis: integer or ``None``
@@ -460,10 +460,10 @@ def topk(x, k, axis=-1, idx_dtype='int64'):
     if axis is None:
         x = theano.tensor.flatten(x)
         axis = -1
-    return TopKOp(axis=axis, idx_dtype=idx_dtype)(x, k)[0]
+    return TopKOp(axis=axis, idx_dtype=idx_dtype)(x, kth)[0]
 
 
-def argtopk(x, k, axis=-1, idx_dtype='int64'):
+def argtopk(x, kth, axis=-1, idx_dtype='int64'):
     """
     Returns the indices of k-largest elements along an axis.
 
@@ -472,7 +472,7 @@ def argtopk(x, k, axis=-1, idx_dtype='int64'):
 
     x: tensor instance
 
-    k: integer constant/variable
+    kth: integer constant/variable
         Must not be 0. If negative, gives k-smallest elements instead.
 
     axis: integer, tuple/list of integers, or ``None``
@@ -499,10 +499,10 @@ def argtopk(x, k, axis=-1, idx_dtype='int64'):
         axis = -1
     return TopKOp(
         axis=axis,
-        idx_dtype=idx_dtype)(x, k)[1]
+        idx_dtype=idx_dtype)(x, kth)[1]
 
 
-def topk_and_argtopk(x, k, axis=-1, idx_dtype='int64'):
+def topk_and_argtopk(x, kth, axis=-1, idx_dtype='int64'):
     """
     Returns the results of both topk() and argtopk() in one Op.
 
@@ -518,4 +518,4 @@ def topk_and_argtopk(x, k, axis=-1, idx_dtype='int64'):
         axis = -1
     return TopKOp(
         axis=axis,
-        idx_dtype=idx_dtype)(x, k)
+        idx_dtype=idx_dtype)(x, kth)

theano/tensor/tests/test_sort.py

@@ -22,7 +22,6 @@ def gen_unique_vector(size, dtype):
     return (retval[np.random.permutation(size)] - size * 1.5).astype(dtype)
 
 
-'''
 class Test_sort(unittest.TestCase):
 
     def setUp(self):
@@ -230,7 +229,6 @@ def test_argsort_grad():
 
     data = np.random.rand(2, 3, 3).astype(theano.config.floatX)
     utt.verify_grad(lambda x: argsort(x, axis=2), [data])
-'''
 
 
 class Test_TopK(unittest.TestCase):
@@ -408,59 +406,13 @@ class Test_TopK(unittest.TestCase):
                 continue
 
             def op(x):
-                return theano.ggtensor.sort(topk(x, k=k, axis=axis), axis=axis)
+                return theano.tensor.sort(topk(x, k, axis=axis), axis=axis)
 
             xval = np.random.rand(*shp).astype(theano.config.floatX)
             utt.verify_grad(op, [xval])
 
 
 class TopKInferShapeTester(utt.InferShapeTester):
-    @utt.parameterized.expand(product(
-        ((15, 17), (11, 7, 5), (2, 3, 5, 7, 11), (2, 4, 3, 1)),
-        (1, '(1+n)//2', 'n-1', '-n')))
-    def test_topk_infer_shape(self, shp, k_):
-        ndim = len(shp)
-        for axis in range(-ndim, ndim):
-            if isinstance(k_, str):
-                k = eval(k_.replace('n', str(shp[axis])))
-            else:
-                k = k_
-
-            if k == 0:
-                continue
-
-            x = theano.tensor.tensor(
-                name='x', broadcastable=(False,) * len(shp),
-                dtype=theano.config.floatX)
-            y = topk(x, k, axis=axis)
-            size = reduce(int.__mul__, shp)
-            xval = gen_unique_vector(size, theano.config.floatX).reshape(shp)
-            self._compile_and_check(
-                [x], [y], [xval], TopKOp)
-
-    @utt.parameterized.expand(product(
-        ((15, 17), (11, 7, 5), (2, 3, 5, 7, 11), (2, 4, 3, 1)),
-        (-1, '(1+n)//2', '1-n')))
-    def test_argtopk_infer_shape(self, shp, k_):
-        ndim = len(shp)
-        for axis in range(-ndim, ndim):
-            if isinstance(k_, str):
-                k = eval(k_.replace('n', str(shp[axis])))
-            else:
-                k = k_
-
-            if k == 0:
-                continue
-
-            x = theano.tensor.tensor(
-                name='x', broadcastable=(False,) * len(shp),
-                dtype=theano.config.floatX)
-            y = argtopk(x, k, axis=axis, idx_dtype='int32')
-            size = reduce(int.__mul__, shp)
-            xval = gen_unique_vector(size, theano.config.floatX).reshape(shp)
-            self._compile_and_check(
-                [x], [y], [xval], TopKOp)
-
     @utt.parameterized.expand(product(
         ((2, 3), (15, 17), (11, 7, 5), (2, 3, 5, 7, 11), (2, 4, 3, 1)),
         (1, '(1+n)//2', 'n-1', 'n')))

theano/tests/unittest_tools.py

@@ -83,7 +83,7 @@ def seed_rng(pseed=None):
 
 def verify_grad(op, pt, n_tests=2, rng=None, *args, **kwargs):
     """
-    Wrapper for tensor/basic.py:verify_grad
+    Wrapper for gradient.py:verify_grad
     Takes care of seeding the random number generator if None is given
     """
     if rng is None: