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提交 501dc901 authored 作者: Olivier Breuleux's avatar Olivier Breuleux
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moving tensor testers

上级 970f985d
隐藏空白字符变更
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正在显示 包含 50 行增加 和 50 行删除
theano/tensor/tests/_test_basic.py → theano/tensor/tests/test_basic.py
浏览文件 @ 501dc901
......@@ -53,7 +53,7 @@ def safe_make_node(op, *inputs):
else:
return node.owner
def make_tester(name, op, expected, checks = {}, good = {}, bad_build = {}, bad_runtime = {}, grad = {}):
def make_restet(name, op, expected, checks = {}, good = {}, bad_build = {}, bad_runtime = {}, grad = {}):
if grad is True:
grad = good
......@@ -192,7 +192,7 @@ def randint_ranged(min, max, shape):
return numpy.random.random_integers(min, max, shape)
def make_broadcast_tester(op, expected, checks = {}, **kwargs):
def make_broadcast_restet(op, expected, checks = {}, **kwargs):
name = str(op) + "Tester"
if kwargs.has_key('inplace'):
if kwargs['inplace']:
......@@ -201,7 +201,7 @@ def make_broadcast_tester(op, expected, checks = {}, **kwargs):
checks = dict(checks,
inplace_check = lambda inputs, outputs: inputs[0] is outputs[0])
del kwargs['inplace']
return make_tester(name, op, expected, checks, **kwargs)
return make_restet(name, op, expected, checks, **kwargs)
......@@ -225,28 +225,28 @@ _grad_broadcast_binary_normal = dict(same_shapes = (rand(2, 3), rand(2, 3)),
column = (rand(2, 3), rand(2, 1)))
AddTester = make_broadcast_tester(op = add,
AddTester = make_broadcast_restet(op = add,
expected = lambda *inputs: reduce(lambda x, y: x + y, inputs),
good = dict(three_inputs_same_shapes = (rand(2, 3), rand(2, 3), rand(2, 3)),
four_inputs_broadcast = (rand(2, 3), rand(1, 3), rand(2, 1), rand(1, 1)),
**_good_broadcast_binary_normal),
bad_build = _bad_build_broadcast_binary_normal,
bad_runtime = _bad_runtime_broadcast_binary_normal)
AddInplaceTester = make_broadcast_tester(op = tensor._add_inplace,
AddInplaceTester = make_broadcast_restet(op = tensor._add_inplace,
expected = lambda x, y: x + y,
good = _good_broadcast_binary_normal,
bad_build = _bad_build_broadcast_binary_normal,
bad_runtime = _bad_runtime_broadcast_binary_normal,
inplace = True)
SubTester = make_broadcast_tester(op = sub,
SubTester = make_broadcast_restet(op = sub,
expected = lambda x, y: x - y,
good = _good_broadcast_binary_normal,
bad_build = _bad_build_broadcast_binary_normal,
bad_runtime = _bad_runtime_broadcast_binary_normal,
grad = _grad_broadcast_binary_normal)
SubInplaceTester = make_broadcast_tester(op = tensor._sub_inplace,
SubInplaceTester = make_broadcast_restet(op = tensor._sub_inplace,
expected = lambda x, y: x - y,
good = _good_broadcast_binary_normal,
bad_build = _bad_build_broadcast_binary_normal,
......@@ -254,7 +254,7 @@ SubInplaceTester = make_broadcast_tester(op = tensor._sub_inplace,
grad = _grad_broadcast_binary_normal,
inplace = True)
MulTester = make_broadcast_tester(op = mul,
MulTester = make_broadcast_restet(op = mul,
expected = lambda *inputs: reduce(lambda x, y: x * y, inputs),
good = dict(three_inputs_same_shapes = (rand(2, 3), rand(2, 3), rand(2, 3)),
four_inputs_broadcast = (rand(2, 3), rand(1, 3), rand(2, 1), rand(1, 1)),
......@@ -264,7 +264,7 @@ MulTester = make_broadcast_tester(op = mul,
grad = dict(three_inputs_same_shapes = (rand(2, 3), rand(2, 3), rand(2, 3)),
four_inputs_broadcast = (rand(2, 3), rand(1, 3), rand(2, 1), rand(1, 1)),
**_grad_broadcast_binary_normal))
MulInplaceTester = make_broadcast_tester(op = tensor._mul_inplace,
MulInplaceTester = make_broadcast_restet(op = tensor._mul_inplace,
expected = lambda x, y: x * y,
good = _good_broadcast_binary_normal,
bad_build = _bad_build_broadcast_binary_normal,
......@@ -272,7 +272,7 @@ MulInplaceTester = make_broadcast_tester(op = tensor._mul_inplace,
grad = _grad_broadcast_binary_normal,
inplace = True)
DivTester = make_broadcast_tester(op = div,
DivTester = make_broadcast_restet(op = div,
expected = lambda x, y: x / y,
good = dict(same_shapes = (rand(2, 3), rand(2, 3)),
scalar = (rand(2, 3), rand(1, 1)),
......@@ -290,7 +290,7 @@ DivTester = make_broadcast_tester(op = div,
scalar = (rand(2, 3), rand(1, 1)),
row = (rand(2, 3), rand(1, 3)),
column = (rand(2, 3), rand(2, 1))))
DivInplaceTester = make_broadcast_tester(op = tensor._div_inplace,
DivInplaceTester = make_broadcast_restet(op = tensor._div_inplace,
expected = lambda x, y: x / y,
good = dict(same_shapes = (rand(2, 3), rand(2, 3)),
scalar = (rand(2, 3), rand(1, 1)),
......@@ -305,7 +305,7 @@ DivInplaceTester = make_broadcast_tester(op = tensor._div_inplace,
column = (rand(2, 3), rand(2, 1))),
inplace = True)
ModTester = make_broadcast_tester(op = mod,
ModTester = make_broadcast_restet(op = mod,
expected = lambda x, y: x % y,
good = dict(same_shapes = (rand(2, 3), rand(2, 3)),
scalar = (rand(2, 3), rand(1, 1)),
......@@ -320,7 +320,7 @@ ModTester = make_broadcast_tester(op = mod,
# dtype_mixup_1 = (rand(2, 3), randint_nonzero(2, 3)),
# dtype_mixup_2 = (randint_nonzero(2, 3), rand(2, 3))),
)
ModInplaceTester = make_broadcast_tester(op = tensor._mod_inplace,
ModInplaceTester = make_broadcast_restet(op = tensor._mod_inplace,
expected = lambda x, y: x % y,
good = dict(same_shapes = (rand(2, 3), rand(2, 3)),
scalar = (rand(2, 3), rand(1, 1)),
......@@ -331,7 +331,7 @@ ModInplaceTester = make_broadcast_tester(op = tensor._mod_inplace,
),
inplace = True)
PowTester = make_broadcast_tester(op = pow,
PowTester = make_broadcast_restet(op = pow,
expected = lambda x, y: x ** y,
good = dict(same_shapes = (rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (2, 3))),
scalar = (rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (1, 1))),
......@@ -343,7 +343,7 @@ PowTester = make_broadcast_tester(op = pow,
row = (rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (1, 3))),
column = (rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (2, 1))))
)
PowInplaceTester = make_broadcast_tester(op = tensor._pow_inplace,
PowInplaceTester = make_broadcast_restet(op = tensor._pow_inplace,
expected = lambda x, y: x ** y,
good = dict(same_shapes = (rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (2, 3))),
scalar = (rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (1, 1))),
......@@ -364,49 +364,49 @@ _good_broadcast_unary_normal = dict(normal = (rand_ranged(-5, 5, (2, 3)),),
_grad_broadcast_unary_normal = dict(normal = (rand_ranged(-5, 5, (2, 3)),))
AbsTester = make_broadcast_tester(op = tensor._abs,
AbsTester = make_broadcast_restet(op = tensor._abs,
expected = lambda x: abs(x),
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal)
AbsInplaceTester = make_broadcast_tester(op = tensor.__abs_inplace,
AbsInplaceTester = make_broadcast_restet(op = tensor.__abs_inplace,
expected = lambda x: numpy.abs(x),
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal,
inplace = True)
NegTester = make_broadcast_tester(op = neg,
NegTester = make_broadcast_restet(op = neg,
expected = lambda x: -x,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal)
NegInplaceTester = make_broadcast_tester(op = tensor._neg_inplace,
NegInplaceTester = make_broadcast_restet(op = tensor._neg_inplace,
expected = lambda x: -x,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal,
inplace = True)
SgnTester = make_broadcast_tester(op = sgn,
SgnTester = make_broadcast_restet(op = sgn,
expected = numpy.sign,
good = _good_broadcast_unary_normal)
SgnInplaceTester = make_broadcast_tester(op = tensor._sgn_inplace,
SgnInplaceTester = make_broadcast_restet(op = tensor._sgn_inplace,
expected = numpy.sign,
good = _good_broadcast_unary_normal,
inplace = True)
SqrTester = make_broadcast_tester(op = sqr,
SqrTester = make_broadcast_restet(op = sqr,
expected = numpy.square,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal)
SqrInplaceTester = make_broadcast_tester(op = tensor._sqr_inplace,
SqrInplaceTester = make_broadcast_restet(op = tensor._sqr_inplace,
expected = numpy.square,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal,
inplace = True)
ExpTester = make_broadcast_tester(op = exp,
ExpTester = make_broadcast_restet(op = exp,
expected = numpy.exp,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal)
ExpInplaceTester = make_broadcast_tester(op = tensor._exp_inplace,
ExpInplaceTester = make_broadcast_restet(op = tensor._exp_inplace,
expected = numpy.exp,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal,
......@@ -418,31 +418,31 @@ _good_broadcast_unary_positive = dict(normal = (rand_ranged(0.001, 5, (2, 3)),),
_grad_broadcast_unary_positive = dict(normal = (rand_ranged(0.001, 5, (2, 3)),))
LogTester = make_broadcast_tester(op = log,
LogTester = make_broadcast_restet(op = log,
expected = numpy.log,
good = _good_broadcast_unary_positive,
grad = _grad_broadcast_unary_positive)
LogInplaceTester = make_broadcast_tester(op = tensor._log_inplace,
LogInplaceTester = make_broadcast_restet(op = tensor._log_inplace,
expected = numpy.log,
good = _good_broadcast_unary_positive,
grad = _grad_broadcast_unary_positive,
inplace = True)
Log2Tester = make_broadcast_tester(op = log2,
Log2Tester = make_broadcast_restet(op = log2,
expected = numpy.log2,
good = _good_broadcast_unary_positive,
grad = _grad_broadcast_unary_positive)
Log2InplaceTester = make_broadcast_tester(op = tensor._log2_inplace,
Log2InplaceTester = make_broadcast_restet(op = tensor._log2_inplace,
expected = numpy.log2,
good = _good_broadcast_unary_positive,
grad = _grad_broadcast_unary_positive,
inplace = True)
SqrtTester = make_broadcast_tester(op = sqrt,
SqrtTester = make_broadcast_restet(op = sqrt,
expected = numpy.sqrt,
good = _good_broadcast_unary_positive,
grad = _grad_broadcast_unary_positive)
SqrtInplaceTester = make_broadcast_tester(op = tensor._sqrt_inplace,
SqrtInplaceTester = make_broadcast_restet(op = tensor._sqrt_inplace,
expected = numpy.sqrt,
good = _good_broadcast_unary_positive,
grad = _grad_broadcast_unary_positive,
......@@ -456,33 +456,33 @@ _good_broadcast_unary_wide = dict(normal = (rand_ranged(-1000, 1000, (2, 3)),),
_grad_broadcast_unary_wide = dict(normal = (rand_ranged(-1000, 1000, (2, 3)),))
SinTester = make_broadcast_tester(op = sin,
SinTester = make_broadcast_restet(op = sin,
expected = numpy.sin,
good = _good_broadcast_unary_wide,
grad = _grad_broadcast_unary_wide)
SinInplaceTester = make_broadcast_tester(op = tensor._sin_inplace,
SinInplaceTester = make_broadcast_restet(op = tensor._sin_inplace,
expected = numpy.sin,
good = _good_broadcast_unary_wide,
grad = _grad_broadcast_unary_wide,
inplace = True)
CosTester = make_broadcast_tester(op = cos,
CosTester = make_broadcast_restet(op = cos,
expected = numpy.cos,
good = _good_broadcast_unary_wide,
grad = _grad_broadcast_unary_wide)
CosInplaceTester = make_broadcast_tester(op = tensor._cos_inplace,
CosInplaceTester = make_broadcast_restet(op = tensor._cos_inplace,
expected = numpy.cos,
good = _good_broadcast_unary_wide,
grad = _grad_broadcast_unary_wide,
inplace = True)
TanTester = make_broadcast_tester(op = tan,
TanTester = make_broadcast_restet(op = tan,
expected = numpy.tan,
good = dict(normal = (rand_ranged(-3.14, 3.14, (2, 3)),),
shifted = (rand_ranged(3.15, 6.28, (2, 3)),)),
grad = dict(normal = (rand_ranged(-3.14, 3.14, (2, 3)),),
shifted = (rand_ranged(3.15, 6.28, (2, 3)),)))
TanInplaceTester = make_broadcast_tester(op = tensor._tan_inplace,
TanInplaceTester = make_broadcast_restet(op = tensor._tan_inplace,
expected = numpy.tan,
good = dict(normal = (rand_ranged(-3.14, 3.14, (2, 3)),),
shifted = (rand_ranged(3.15, 6.28, (2, 3)),)),
......@@ -491,31 +491,31 @@ TanInplaceTester = make_broadcast_tester(op = tensor._tan_inplace,
inplace = True)
CoshTester = make_broadcast_tester(op = cosh,
CoshTester = make_broadcast_restet(op = cosh,
expected = numpy.cosh,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal)
CoshInplaceTester = make_broadcast_tester(op = tensor._cosh_inplace,
CoshInplaceTester = make_broadcast_restet(op = tensor._cosh_inplace,
expected = numpy.cosh,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal,
inplace = True)
SinhTester = make_broadcast_tester(op = sinh,
SinhTester = make_broadcast_restet(op = sinh,
expected = numpy.sinh,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal)
SinhInplaceTester = make_broadcast_tester(op = tensor._sinh_inplace,
SinhInplaceTester = make_broadcast_restet(op = tensor._sinh_inplace,
expected = numpy.sinh,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal,
inplace = True)
TanhTester = make_broadcast_tester(op = tanh,
TanhTester = make_broadcast_restet(op = tanh,
expected = numpy.tanh,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal)
TanhInplaceTester = make_broadcast_tester(op = tensor._tanh_inplace,
TanhInplaceTester = make_broadcast_restet(op = tensor._tanh_inplace,
expected = numpy.tanh,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal,
......@@ -523,7 +523,7 @@ TanhInplaceTester = make_broadcast_tester(op = tensor._tanh_inplace,
DotTester = make_tester(name = 'DotTester',
DotTester = make_restet(name = 'DotTester',
op = dot,
expected = lambda x, y: numpy.dot(x, y),
checks = {},
......
theano/tensor/tests/_test_elemwise.py → theano/tensor/tests/test_elemwise.py
浏览文件 @ 501dc901
......@@ -15,7 +15,7 @@ def Env(i, o):
e = gof.Env(i, o)
return e
class _test_DimShuffle(unittest.TestCase):
class test_DimShuffle(unittest.TestCase):
def with_linker(self, linker):
for xsh, shuffle, zsh in [((2, 3), (1, 'x', 0), (3, 1, 2)),
......@@ -35,7 +35,7 @@ class _test_DimShuffle(unittest.TestCase):
self.with_linker(gof.PerformLinker())
class _test_Broadcast(unittest.TestCase):
class test_Broadcast(unittest.TestCase):
def with_linker(self, linker):
for xsh, ysh in [((3, 5), (3, 5)),
......@@ -119,7 +119,7 @@ class _test_Broadcast(unittest.TestCase):
assert (f(xv) == zv).all()
class _test_CAReduce(unittest.TestCase):
class test_CAReduce(unittest.TestCase):
def with_linker(self, linker):
for xsh, tosum in [((5, 6), None),
......
theano/tensor/tests/_test_opt.py → theano/tensor/tests/test_opt.py
浏览文件 @ 501dc901
......@@ -61,7 +61,7 @@ def inputs(xbc = (0, 0), ybc = (0, 0), zbc = (0, 0)):
ds = lambda x, y: DimShuffle(x.type.broadcastable, y)(x)
class _test_dimshuffle_lift(unittest.TestCase):
class test_dimshuffle_lift(unittest.TestCase):
def test_double_transpose(self):
x, y, z = inputs()
......@@ -102,7 +102,7 @@ from theano.tensor import *
#from sandbox import pprint
class _test_greedy_distribute(unittest.TestCase):
class test_greedy_distribute(unittest.TestCase):
def test_main(self):
a, b, c, d, x, y, z = matrices('abcdxyz')
e = (a/z + b/x) * x * z
......@@ -115,7 +115,7 @@ class _test_greedy_distribute(unittest.TestCase):
class _test_canonize(unittest.TestCase):
class test_canonize(unittest.TestCase):
def test_muldiv(self):
x, y, z = matrices('xyz')
......
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