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提交 8910e6eb authored 作者: lamblin's avatar lamblin
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Merge pull request #1261 from nouiz/adv_sub1

Adv sub1: allow broadcasted index vector.
上级 ad8a1755 477370da
隐藏空白字符变更
内嵌 并排
正在显示 包含 89 行增加 和 65 行删除
theano/sandbox/cuda/basic_ops.py
浏览文件 @ 8910e6eb
...@@ -2322,7 +2322,7 @@ class GpuAdvancedSubtensor1(tensor.AdvancedSubtensor1, GpuOp): ...@@ -2322,7 +2322,7 @@ class GpuAdvancedSubtensor1(tensor.AdvancedSubtensor1, GpuOp):
ilist_ = tensor.as_tensor_variable(ilist) ilist_ = tensor.as_tensor_variable(ilist)
if ilist_.type.dtype[:3] not in ('int', 'uin'): if ilist_.type.dtype[:3] not in ('int', 'uin'):
raise TypeError('index must be integers') raise TypeError('index must be integers')
if ilist_.type.broadcastable != (False,): if ilist_.type.ndim != 1:
raise TypeError('index must be vector') raise TypeError('index must be vector')
if x_.type.ndim == 0: if x_.type.ndim == 0:
raise TypeError('cannot index into a scalar') raise TypeError('cannot index into a scalar')
......
theano/tensor/basic.py
浏览文件 @ 8910e6eb
...@@ -6896,7 +6896,7 @@ class AdvancedSubtensor1(Op): ...@@ -6896,7 +6896,7 @@ class AdvancedSubtensor1(Op):
ilist_ = as_tensor_variable(ilist) ilist_ = as_tensor_variable(ilist)
if ilist_.type.dtype[:3] not in ('int', 'uin'): if ilist_.type.dtype[:3] not in ('int', 'uin'):
raise TypeError('index must be integers') raise TypeError('index must be integers')
if ilist_.type.broadcastable != (False,): if ilist_.type.ndim != 1:
raise TypeError('index must be vector') raise TypeError('index must be vector')
if x_.type.ndim == 0: if x_.type.ndim == 0:
raise TypeError('cannot index into a scalar') raise TypeError('cannot index into a scalar')
......
theano/tensor/tests/test_basic.py
浏览文件 @ 8910e6eb
...@@ -39,13 +39,12 @@ from theano.tensor import (_shared, wvector, bvector, autocast_float_as, ...@@ -39,13 +39,12 @@ from theano.tensor import (_shared, wvector, bvector, autocast_float_as,
get_scalar_constant_value, ivector, reshape, scalar_from_tensor, scal, get_scalar_constant_value, ivector, reshape, scalar_from_tensor, scal,
iscalars, arange, dscalars, fvector, imatrix, numeric_grad, iscalars, arange, dscalars, fvector, imatrix, numeric_grad,
opt, ComplexError, lvector, true_div, max, min, Split, roll, opt, ComplexError, lvector, true_div, max, min, Split, roll,
tile, patternbroadcast, Eye, Shape, Default, Dot, PermuteRowElements, tile, patternbroadcast, Eye, Shape, Dot, PermuteRowElements,
ScalarFromTensor, TensorFromScalar, dtensor4, Rebroadcast, Alloc, ScalarFromTensor, TensorFromScalar, dtensor4, Rebroadcast, Alloc,
dtensor3, SpecifyShape, Mean, IncSubtensor, AdvancedIncSubtensor1, dtensor3, SpecifyShape, Mean, IncSubtensor, AdvancedIncSubtensor1,
itensor3, Tile, AdvancedIncSubtensor, switch, Diagonal, Diag, itensor3, Tile, AdvancedIncSubtensor, switch, Diagonal, Diag,
nonzero, flatnonzero, nonzero_values) nonzero, flatnonzero, nonzero_values)
from theano.tests import unittest_tools as utt from theano.tests import unittest_tools as utt
from theano.printing import debugprint
imported_scipy_special = False imported_scipy_special = False
...@@ -615,12 +614,12 @@ SubTester = makeBroadcastTester( ...@@ -615,12 +614,12 @@ SubTester = makeBroadcastTester(
grad=_grad_broadcast_binary_normal) grad=_grad_broadcast_binary_normal)
SubInplaceTester = makeBroadcastTester(op=inplace.sub_inplace, SubInplaceTester = makeBroadcastTester(op=inplace.sub_inplace,
expected=lambda x, y: x - y, expected=lambda x, y: x - y,
good=_good_broadcast_binary_normal, good=_good_broadcast_binary_normal,
bad_build = _bad_build_broadcast_binary_normal, bad_build=_bad_build_broadcast_binary_normal,
bad_runtime = _bad_runtime_broadcast_binary_normal, bad_runtime=_bad_runtime_broadcast_binary_normal,
grad = _grad_broadcast_binary_normal, grad=_grad_broadcast_binary_normal,
inplace = True) inplace=True)
SwitchTester = makeBroadcastTester( SwitchTester = makeBroadcastTester(
...@@ -902,7 +901,8 @@ _good_broadcast_unary_normal_float_no_complex = copymod( ...@@ -902,7 +901,8 @@ _good_broadcast_unary_normal_float_no_complex = copymod(
without=['complex']) without=['complex'])
_good_broadcast_unary_normal = dict( _good_broadcast_unary_normal = dict(
normal=[numpy.asarray(rand_ranged(-5, 5, (2, 3)), dtype=config.floatX)], normal=[numpy.asarray(rand_ranged(-5, 5, (2, 3)),
dtype=config.floatX)],
integers=[randint_ranged(-5, 5, (2, 3))], integers=[randint_ranged(-5, 5, (2, 3))],
corner_case=[corner_case], corner_case=[corner_case],
complex=[randcomplex(2, 3)], complex=[randcomplex(2, 3)],
...@@ -944,9 +944,9 @@ _good_broadcast_unary_normal_abs = copy(_good_broadcast_unary_normal) ...@@ -944,9 +944,9 @@ _good_broadcast_unary_normal_abs = copy(_good_broadcast_unary_normal)
del _good_broadcast_unary_normal_abs['complex'] del _good_broadcast_unary_normal_abs['complex']
AbsInplaceTester = makeBroadcastTester(op=inplace.abs__inplace, AbsInplaceTester = makeBroadcastTester(op=inplace.abs__inplace,
expected=lambda x: numpy.abs(x), expected=lambda x: numpy.abs(x),
good = _good_broadcast_unary_normal_abs, good=_good_broadcast_unary_normal_abs,
grad = _grad_broadcast_unary_normal, grad=_grad_broadcast_unary_normal,
inplace = True) inplace=True)
NegTester = makeBroadcastTester(op=tensor.neg, NegTester = makeBroadcastTester(op=tensor.neg,
expected=lambda x: -x, expected=lambda x: -x,
...@@ -964,9 +964,9 @@ SgnTester = makeBroadcastTester(op=tensor.sgn, ...@@ -964,9 +964,9 @@ SgnTester = makeBroadcastTester(op=tensor.sgn,
grad=_grad_broadcast_unary_normal,) grad=_grad_broadcast_unary_normal,)
SgnInplaceTester = makeBroadcastTester(op=inplace.sgn_inplace, SgnInplaceTester = makeBroadcastTester(op=inplace.sgn_inplace,
expected=numpy.sign, expected=numpy.sign,
good = _good_broadcast_unary_normal_no_complex, good=_good_broadcast_unary_normal_no_complex,
grad = _grad_broadcast_unary_normal, grad=_grad_broadcast_unary_normal,
inplace = True) inplace=True)
IntDivTester = makeBroadcastTester( IntDivTester = makeBroadcastTester(
...@@ -1602,16 +1602,18 @@ DotTester = makeTester(name='DotTester', ...@@ -1602,16 +1602,18 @@ DotTester = makeTester(name='DotTester',
rand(5, 7)), rand(5, 7)),
mixed2=(rand(5).astype('float64'), mixed2=(rand(5).astype('float64'),
rand(5, 7)), rand(5, 7)),
complex1 = (randcomplex(5, 7), randcomplex(7)), complex1=(randcomplex(5, 7),
complex2 = (rand(5, 7), randcomplex(7)), randcomplex(7)),
complex3 = (randcomplex(5, 7), rand(7)), complex2=(rand(5, 7), randcomplex(7)),
empty1 = (numpy.asarray([]),numpy.asarray([])), complex3=(randcomplex(5, 7), rand(7)),
empty2 = (rand(5,0),rand(0,2)), empty1=(numpy.asarray([]),
empty3 = (rand(0,5),rand(5,0)), numpy.asarray([])),
empty2=(rand(5, 0), rand(0, 2)),
empty3=(rand(0, 5), rand(5, 0)),
), ),
bad_build = dict(), bad_build=dict(),
bad_runtime = dict(bad1 = (rand(5, 7), rand(5, 7)), bad_runtime=dict(bad1=(rand(5, 7), rand(5, 7)),
bad2 = (rand(5, 7), rand(8, 3)))) bad2=(rand(5, 7), rand(8, 3))))
def _numpy_second(x, y): def _numpy_second(x, y):
...@@ -1678,8 +1680,9 @@ SecondSameRankTester = makeTester( ...@@ -1678,8 +1680,9 @@ SecondSameRankTester = makeTester(
multi_dtype_checks((4, 5), (5, 4)), multi_dtype_checks((4, 5), (5, 4)),
multi_dtype_checks((1, 5), (5, 4)), multi_dtype_checks((1, 5), (5, 4)),
)), )),
mode=get_default_mode().excluding('local_fill_to_alloc', mode=get_default_mode().excluding(
'local_useless_fill') 'local_fill_to_alloc',
'local_useless_fill')
) )
### Alloc ### Alloc
...@@ -1869,7 +1872,6 @@ class test_triangle(unittest.TestCase): ...@@ -1869,7 +1872,6 @@ class test_triangle(unittest.TestCase):
yield check, dtype, 5, 3, 1 yield check, dtype, 5, 3, 1
yield check, dtype, 5, 3, -1 yield check, dtype, 5, 3, -1
def test_tril_triu(self): def test_tril_triu(self):
def check_l(m, k=0): def check_l(m, k=0):
m_symb = matrix(dtype=m.dtype) m_symb = matrix(dtype=m.dtype)
...@@ -1911,12 +1913,13 @@ class test_nonzero(unittest.TestCase): ...@@ -1911,12 +1913,13 @@ class test_nonzero(unittest.TestCase):
def test_nonzero(self): def test_nonzero(self):
def check(m): def check(m):
m_symb = theano.tensor.tensor(dtype=m.dtype, m_symb = theano.tensor.tensor(dtype=m.dtype,
broadcastable = (False,) * m.ndim) broadcastable=(False,) * m.ndim)
f_tuple = function([m_symb], nonzero(m_symb, return_matrix=False)) f_tuple = function([m_symb], nonzero(m_symb, return_matrix=False))
f_matrix = function([m_symb], nonzero(m_symb, return_matrix=True)) f_matrix = function([m_symb], nonzero(m_symb, return_matrix=True))
self.assertTrue(numpy.allclose(f_matrix(m), numpy.vstack(numpy.nonzero(m)))) self.assertTrue(numpy.allclose(f_matrix(m),
numpy.vstack(numpy.nonzero(m))))
for i, j in zip(f_tuple(m), numpy.nonzero(m)): for i, j in zip(f_tuple(m), numpy.nonzero(m)):
self.assertTrue(numpy.allclose(i, j)) self.assertTrue(numpy.allclose(i, j))
...@@ -1939,11 +1942,10 @@ class test_nonzero(unittest.TestCase): ...@@ -1939,11 +1942,10 @@ class test_nonzero(unittest.TestCase):
rand4d[:4] = 0 rand4d[:4] = 0
check(rand4d) check(rand4d)
def test_flatnonzero(self): def test_flatnonzero(self):
def check(m): def check(m):
m_symb = theano.tensor.tensor(dtype=m.dtype, m_symb = theano.tensor.tensor(dtype=m.dtype,
broadcastable = (False,) * m.ndim) broadcastable=(False,) * m.ndim)
f = function([m_symb], flatnonzero(m_symb)) f = function([m_symb], flatnonzero(m_symb))
result = f(m) result = f(m)
assert numpy.allclose(result, numpy.flatnonzero(m)) assert numpy.allclose(result, numpy.flatnonzero(m))
...@@ -1970,7 +1972,7 @@ class test_nonzero(unittest.TestCase): ...@@ -1970,7 +1972,7 @@ class test_nonzero(unittest.TestCase):
def test_nonzero_values(self): def test_nonzero_values(self):
def check(m): def check(m):
m_symb = theano.tensor.tensor(dtype=m.dtype, m_symb = theano.tensor.tensor(dtype=m.dtype,
broadcastable = (False,) * m.ndim) broadcastable=(False,) * m.ndim)
f = function([m_symb], nonzero_values(m_symb)) f = function([m_symb], nonzero_values(m_symb))
result = f(m) result = f(m)
assert numpy.allclose(result, m[numpy.nonzero(m)]) assert numpy.allclose(result, m[numpy.nonzero(m)])
...@@ -2104,6 +2106,7 @@ def _approx_eq(a, b, eps=1.0e-4): ...@@ -2104,6 +2106,7 @@ def _approx_eq(a, b, eps=1.0e-4):
return True return True
_approx_eq.debug = 0 _approx_eq.debug = 0
def test_batched_dot(): def test_batched_dot():
first = theano.tensor.tensor3("first") first = theano.tensor.tensor3("first")
second = theano.tensor.tensor3("second") second = theano.tensor.tensor3("second")
...@@ -2126,6 +2129,7 @@ def test_batched_dot(): ...@@ -2126,6 +2129,7 @@ def test_batched_dot():
assert result.shape[0] == first_val.shape[0] assert result.shape[0] == first_val.shape[0]
def test_tensor_values_eq_approx(): def test_tensor_values_eq_approx():
#test, inf, -inf and nan equal themself #test, inf, -inf and nan equal themself
a = numpy.asarray([-numpy.inf, -1, 0, 1, numpy.inf, numpy.nan]) a = numpy.asarray([-numpy.inf, -1, 0, 1, numpy.inf, numpy.nan])
...@@ -3168,6 +3172,23 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin): ...@@ -3168,6 +3172,23 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
self.assertTrue(numpy.allclose(f([0]), ones[0] * 5)) self.assertTrue(numpy.allclose(f([0]), ones[0] * 5))
self.assertRaises(IndexError, f, [0, 1]) self.assertRaises(IndexError, f, [0, 1])
def test_adv_sub1_idx_broadcast(self):
# The idx can be a broadcastable vector.
ones = numpy.ones((4, 3), dtype=self.dtype)
n = self.shared(ones * 5)
idx = tensor.TensorType(dtype='int64', broadcastable=(True,))()
assert idx.type.broadcastable == (True,)
t = n[idx]
self.assertTrue(isinstance(t.owner.op, tensor.AdvancedSubtensor1))
f = self.function([idx], t, op=self.adv_sub1)
topo = f.maker.fgraph.toposort()
topo_ = [node for node in topo if not isinstance(node.op,
self.ignore_topo)]
assert len(topo_) == 1
self.assertTrue(isinstance(topo_[0].op, self.adv_sub1))
self.assertTrue(numpy.allclose(f([0]), ones[0] * 5))
def test_shape_i_const(self): def test_shape_i_const(self):
# Each axis is treated independently by shape_i/shape operators # Each axis is treated independently by shape_i/shape operators
...@@ -3179,8 +3200,8 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin): ...@@ -3179,8 +3200,8 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
for stop in [None] + [-8, -5, -1, 0, 1, 5, 8]: for stop in [None] + [-8, -5, -1, 0, 1, 5, 8]:
for step in [None] + [-3, -1, 2]: for step in [None] + [-3, -1, 2]:
outs += [data[start:stop:step].shape] outs += [data[start:stop:step].shape]
shapes += [data.get_value(borrow= shapes += [data.get_value(
True)[start:stop:step].shape] borrow=True)[start:stop:step].shape]
f = self.function([], outs, mode=mode_opt, f = self.function([], outs, mode=mode_opt,
op=self.ops, N=0) op=self.ops, N=0)
t_shapes = f() t_shapes = f()
...@@ -3209,8 +3230,8 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin): ...@@ -3209,8 +3230,8 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
for start in [-8, -5, -4, -1, 0, 1, 4, 5, 8]: for start in [-8, -5, -4, -1, 0, 1, 4, 5, 8]:
for stop in [-8, -5, -4, -1, 0, 1, 4, 5, 8]: for stop in [-8, -5, -4, -1, 0, 1, 4, 5, 8]:
for step in [-3, -1, 2, 5]: for step in [-3, -1, 2, 5]:
assert numpy.all( assert numpy.all(f(start, stop, step) ==
f(start,stop,step) == v_data[start:stop:step].shape) v_data[start:stop:step].shape)
def test_slice_canonical_form_0(self): def test_slice_canonical_form_0(self):
start = tensor.iscalar('b') start = tensor.iscalar('b')
...@@ -4477,10 +4498,10 @@ class t_dot(unittest.TestCase): ...@@ -4477,10 +4498,10 @@ class t_dot(unittest.TestCase):
self.cmp_dot(rand(), rand(5)) self.cmp_dot(rand(), rand(5))
def test_dot_0d_2d(self): def test_dot_0d_2d(self):
self.cmp_dot(rand(), rand(6,7)) self.cmp_dot(rand(), rand(6, 7))
def test_dot_0d_3d(self): def test_dot_0d_3d(self):
self.cmp_dot(rand(), rand(8,6,7)) self.cmp_dot(rand(), rand(8, 6, 7))
def test_dot_1d_0d(self): def test_dot_1d_0d(self):
self.cmp_dot(rand(5), rand()) self.cmp_dot(rand(5), rand())
...@@ -4512,10 +4533,10 @@ class t_dot(unittest.TestCase): ...@@ -4512,10 +4533,10 @@ class t_dot(unittest.TestCase):
self.cmp_dot(rand(0), rand(0, 0)) self.cmp_dot(rand(0), rand(0, 0))
def test_dot_1d_3d(self): def test_dot_1d_3d(self):
self.cmp_dot(rand(6), rand(8,6,7)) self.cmp_dot(rand(6), rand(8, 6, 7))
def test_dot_2d_0d(self): def test_dot_2d_0d(self):
self.cmp_dot(rand(5,6), rand()) self.cmp_dot(rand(5, 6), rand())
def test_dot_2d_1d(self): def test_dot_2d_1d(self):
self.cmp_dot(rand(5, 6), rand(6)) self.cmp_dot(rand(5, 6), rand(6))
...@@ -4548,19 +4569,19 @@ class t_dot(unittest.TestCase): ...@@ -4548,19 +4569,19 @@ class t_dot(unittest.TestCase):
self.cmp_dot(rand(0, 6), rand(6, 0)) self.cmp_dot(rand(0, 6), rand(6, 0))
def test_dot_2d_3d(self): def test_dot_2d_3d(self):
self.cmp_dot(rand(5,6), rand(8,6,7)) self.cmp_dot(rand(5, 6), rand(8, 6, 7))
def test_dot_3d_0d(self): def test_dot_3d_0d(self):
self.cmp_dot(rand(4,5,6), rand()) self.cmp_dot(rand(4, 5, 6), rand())
def test_dot_3d_1d(self): def test_dot_3d_1d(self):
self.cmp_dot(rand(4,5,6), rand(6)) self.cmp_dot(rand(4, 5, 6), rand(6))
def test_dot_3d_2d(self): def test_dot_3d_2d(self):
self.cmp_dot(rand(4,5,6), rand(6,7)) self.cmp_dot(rand(4, 5, 6), rand(6, 7))
def test_dot_3d_3d(self): def test_dot_3d_3d(self):
self.cmp_dot(rand(4,5,6), rand(8,6,7)) self.cmp_dot(rand(4, 5, 6), rand(8, 6, 7))
def not_aligned(self, x, y): def not_aligned(self, x, y):
ctv_backup = config.compute_test_value ctv_backup = config.compute_test_value
...@@ -4602,7 +4623,7 @@ class t_dot(unittest.TestCase): ...@@ -4602,7 +4623,7 @@ class t_dot(unittest.TestCase):
self.not_aligned(rand(5), rand(6, 4)) self.not_aligned(rand(5), rand(6, 4))
def test_align_1_3(self): def test_align_1_3(self):
self.not_aligned(rand(5), rand(6,4,7)) self.not_aligned(rand(5), rand(6, 4, 7))
def test_align_2_1(self): def test_align_2_1(self):
self.not_aligned(rand(5, 4), rand(6)) self.not_aligned(rand(5, 4), rand(6))
...@@ -4611,16 +4632,16 @@ class t_dot(unittest.TestCase): ...@@ -4611,16 +4632,16 @@ class t_dot(unittest.TestCase):
self.not_aligned(rand(5, 4), rand(6, 7)) self.not_aligned(rand(5, 4), rand(6, 7))
def test_align_2_3(self): def test_align_2_3(self):
self.not_aligned(rand(5,4), rand(6,7,8)) self.not_aligned(rand(5, 4), rand(6, 7, 8))
def test_align_3_1(self): def test_align_3_1(self):
self.not_aligned(rand(5,4,3), rand(6)) self.not_aligned(rand(5, 4, 3), rand(6))
def test_align_3_2(self): def test_align_3_2(self):
self.not_aligned(rand(5,4,3), rand(6,7)) self.not_aligned(rand(5, 4, 3), rand(6, 7))
def test_align_3_3(self): def test_align_3_3(self):
self.not_aligned(rand(5,4,3), rand(6,7,8)) self.not_aligned(rand(5, 4, 3), rand(6, 7, 8))
def test_grad(self): def test_grad(self):
utt.verify_grad(dot, [rand(2, 3), rand(3, 2)]) utt.verify_grad(dot, [rand(2, 3), rand(3, 2)])
...@@ -4628,14 +4649,14 @@ class t_dot(unittest.TestCase): ...@@ -4628,14 +4649,14 @@ class t_dot(unittest.TestCase):
utt.verify_grad(dot, [rand(3, 2), rand(2)]) utt.verify_grad(dot, [rand(3, 2), rand(2)])
utt.verify_grad(dot, [rand(2), rand(2)]) utt.verify_grad(dot, [rand(2), rand(2)])
utt.verify_grad(dot, [rand(), rand(2)]) utt.verify_grad(dot, [rand(), rand(2)])
utt.verify_grad(dot, [rand(), rand(2,5)]) utt.verify_grad(dot, [rand(), rand(2, 5)])
utt.verify_grad(dot, [rand(2), rand()]) utt.verify_grad(dot, [rand(2), rand()])
utt.verify_grad(dot, [rand(2,5), rand()]) utt.verify_grad(dot, [rand(2, 5), rand()])
utt.verify_grad(dot, [rand(2,3,4), rand(4)]) utt.verify_grad(dot, [rand(2, 3, 4), rand(4)])
utt.verify_grad(dot, [rand(3), rand(2,3,4)]) utt.verify_grad(dot, [rand(3), rand(2, 3, 4)])
utt.verify_grad(dot, [rand(4,3), rand(2,3,4)]) utt.verify_grad(dot, [rand(4, 3), rand(2, 3, 4)])
utt.verify_grad(dot, [rand(2,3,4), rand(4,5)]) utt.verify_grad(dot, [rand(2, 3, 4), rand(4, 5)])
utt.verify_grad(dot, [rand(2,3,4), rand(3,4,5)]) utt.verify_grad(dot, [rand(2, 3, 4), rand(3, 4, 5)])
def test_broadcastable_patterns(self): def test_broadcastable_patterns(self):
...@@ -4650,7 +4671,8 @@ class t_dot(unittest.TestCase): ...@@ -4650,7 +4671,8 @@ class t_dot(unittest.TestCase):
# This strange way of doing things is the only way that worked on # This strange way of doing things is the only way that worked on
# numpy 1.4.1 # numpy 1.4.1
if r.ndim == 0: if r.ndim == 0:
return numpy.asarray(numpy.complex(1.1, 2.1), dtype=r.dtype) return numpy.asarray(numpy.complex(1.1, 2.1),
dtype=r.dtype)
if r.ndim == 1: if r.ndim == 1:
if r.dtype == 'complex64': if r.dtype == 'complex64':
return numpy.complex64([numpy.complex(1.2, 2.2)]) return numpy.complex64([numpy.complex(1.2, 2.2)])
...@@ -4680,8 +4702,8 @@ class t_dot(unittest.TestCase): ...@@ -4680,8 +4702,8 @@ class t_dot(unittest.TestCase):
x = TensorType(dtype=dtype0, broadcastable=bc0)() x = TensorType(dtype=dtype0, broadcastable=bc0)()
y = TensorType(dtype=dtype1, broadcastable=bc1)() y = TensorType(dtype=dtype1, broadcastable=bc1)()
z = dot(x, y) z = dot(x, y)
t = TensorType(dtype= t = TensorType(dtype=dtype0,
dtype0, broadcastable=z.broadcastable)() broadcastable=z.broadcastable)()
rval = z * 3 + 2 * t rval = z * 3 + 2 * t
f = function([x, y, t], rval) f = function([x, y, t], rval)
...@@ -5004,8 +5026,10 @@ def test_make_column_matrix_broadcastable(): ...@@ -5004,8 +5026,10 @@ def test_make_column_matrix_broadcastable():
def test_flatten_outdimNone(): def test_flatten_outdimNone():
""" Flatten always returns a copy of the array. There is no danger with in-place """Flatten always returns a copy of the array. There is no danger
operations and thus no need to test it.""" with in-place operations and thus no need to test it.
"""
a = dmatrix() a = dmatrix()
c = flatten(a) c = flatten(a)
...@@ -5612,7 +5636,7 @@ class test_tensordot(unittest.TestCase): ...@@ -5612,7 +5636,7 @@ class test_tensordot(unittest.TestCase):
Since tensordot is no longer an op, mimic the old op signature Since tensordot is no longer an op, mimic the old op signature
to allow easy use of verify_grad. to allow easy use of verify_grad.
""" """
return lambda a, b : tensordot(a, b, axes) return lambda a, b: tensordot(a, b, axes)
def setUp(self): def setUp(self):
utt.seed_rng() utt.seed_rng()
......
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