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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
内嵌 并排
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
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...@@ -53,7 +53,7 @@ def safe_make_node(op, *inputs): ...@@ -53,7 +53,7 @@ def safe_make_node(op, *inputs):
else: else:
return node.owner 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: if grad is True:
grad = good grad = good
...@@ -192,7 +192,7 @@ def randint_ranged(min, max, shape): ...@@ -192,7 +192,7 @@ def randint_ranged(min, max, shape):
return numpy.random.random_integers(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" name = str(op) + "Tester"
if kwargs.has_key('inplace'): if kwargs.has_key('inplace'):
if kwargs['inplace']: if kwargs['inplace']:
...@@ -201,7 +201,7 @@ def make_broadcast_tester(op, expected, checks = {}, **kwargs): ...@@ -201,7 +201,7 @@ def make_broadcast_tester(op, expected, checks = {}, **kwargs):
checks = dict(checks, checks = dict(checks,
inplace_check = lambda inputs, outputs: inputs[0] is outputs[0]) inplace_check = lambda inputs, outputs: inputs[0] is outputs[0])
del kwargs['inplace'] 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)), ...@@ -225,28 +225,28 @@ _grad_broadcast_binary_normal = dict(same_shapes = (rand(2, 3), rand(2, 3)),
column = (rand(2, 3), rand(2, 1))) 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), 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)), 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)), four_inputs_broadcast = (rand(2, 3), rand(1, 3), rand(2, 1), rand(1, 1)),
**_good_broadcast_binary_normal), **_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)
AddInplaceTester = make_broadcast_tester(op = tensor._add_inplace, AddInplaceTester = make_broadcast_restet(op = tensor._add_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,
inplace = True) inplace = True)
SubTester = make_broadcast_tester(op = sub, SubTester = make_broadcast_restet(op = sub,
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)
SubInplaceTester = make_broadcast_tester(op = tensor._sub_inplace, SubInplaceTester = make_broadcast_restet(op = tensor._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,
...@@ -254,7 +254,7 @@ SubInplaceTester = make_broadcast_tester(op = tensor._sub_inplace, ...@@ -254,7 +254,7 @@ SubInplaceTester = make_broadcast_tester(op = tensor._sub_inplace,
grad = _grad_broadcast_binary_normal, grad = _grad_broadcast_binary_normal,
inplace = True) inplace = True)
MulTester = make_broadcast_tester(op = mul, MulTester = make_broadcast_restet(op = mul,
expected = lambda *inputs: reduce(lambda x, y: x * y, inputs), 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)), 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)), 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, ...@@ -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)), 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)), four_inputs_broadcast = (rand(2, 3), rand(1, 3), rand(2, 1), rand(1, 1)),
**_grad_broadcast_binary_normal)) **_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, 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,
...@@ -272,7 +272,7 @@ MulInplaceTester = make_broadcast_tester(op = tensor._mul_inplace, ...@@ -272,7 +272,7 @@ MulInplaceTester = make_broadcast_tester(op = tensor._mul_inplace,
grad = _grad_broadcast_binary_normal, grad = _grad_broadcast_binary_normal,
inplace = True) inplace = True)
DivTester = make_broadcast_tester(op = div, DivTester = make_broadcast_restet(op = div,
expected = lambda x, y: x / y, expected = lambda x, y: x / y,
good = dict(same_shapes = (rand(2, 3), rand(2, 3)), good = dict(same_shapes = (rand(2, 3), rand(2, 3)),
scalar = (rand(2, 3), rand(1, 1)), scalar = (rand(2, 3), rand(1, 1)),
...@@ -290,7 +290,7 @@ DivTester = make_broadcast_tester(op = div, ...@@ -290,7 +290,7 @@ DivTester = make_broadcast_tester(op = div,
scalar = (rand(2, 3), rand(1, 1)), scalar = (rand(2, 3), rand(1, 1)),
row = (rand(2, 3), rand(1, 3)), row = (rand(2, 3), rand(1, 3)),
column = (rand(2, 3), rand(2, 1)))) 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, expected = lambda x, y: x / y,
good = dict(same_shapes = (rand(2, 3), rand(2, 3)), good = dict(same_shapes = (rand(2, 3), rand(2, 3)),
scalar = (rand(2, 3), rand(1, 1)), scalar = (rand(2, 3), rand(1, 1)),
...@@ -305,7 +305,7 @@ DivInplaceTester = make_broadcast_tester(op = tensor._div_inplace, ...@@ -305,7 +305,7 @@ DivInplaceTester = make_broadcast_tester(op = tensor._div_inplace,
column = (rand(2, 3), rand(2, 1))), column = (rand(2, 3), rand(2, 1))),
inplace = True) inplace = True)
ModTester = make_broadcast_tester(op = mod, ModTester = make_broadcast_restet(op = mod,
expected = lambda x, y: x % y, expected = lambda x, y: x % y,
good = dict(same_shapes = (rand(2, 3), rand(2, 3)), good = dict(same_shapes = (rand(2, 3), rand(2, 3)),
scalar = (rand(2, 3), rand(1, 1)), scalar = (rand(2, 3), rand(1, 1)),
...@@ -320,7 +320,7 @@ ModTester = make_broadcast_tester(op = mod, ...@@ -320,7 +320,7 @@ ModTester = make_broadcast_tester(op = mod,
# dtype_mixup_1 = (rand(2, 3), randint_nonzero(2, 3)), # dtype_mixup_1 = (rand(2, 3), randint_nonzero(2, 3)),
# dtype_mixup_2 = (randint_nonzero(2, 3), rand(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, expected = lambda x, y: x % y,
good = dict(same_shapes = (rand(2, 3), rand(2, 3)), good = dict(same_shapes = (rand(2, 3), rand(2, 3)),
scalar = (rand(2, 3), rand(1, 1)), scalar = (rand(2, 3), rand(1, 1)),
...@@ -331,7 +331,7 @@ ModInplaceTester = make_broadcast_tester(op = tensor._mod_inplace, ...@@ -331,7 +331,7 @@ ModInplaceTester = make_broadcast_tester(op = tensor._mod_inplace,
), ),
inplace = True) inplace = True)
PowTester = make_broadcast_tester(op = pow, PowTester = make_broadcast_restet(op = pow,
expected = lambda x, y: x ** y, expected = lambda x, y: x ** y,
good = dict(same_shapes = (rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (2, 3))), 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))), scalar = (rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (1, 1))),
...@@ -343,7 +343,7 @@ PowTester = make_broadcast_tester(op = pow, ...@@ -343,7 +343,7 @@ PowTester = make_broadcast_tester(op = pow,
row = (rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (1, 3))), 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)))) 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, expected = lambda x, y: x ** y,
good = dict(same_shapes = (rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (2, 3))), 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))), 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)),), ...@@ -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)),)) _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), expected = lambda x: abs(x),
good = _good_broadcast_unary_normal, good = _good_broadcast_unary_normal,
grad = _grad_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), expected = lambda x: numpy.abs(x),
good = _good_broadcast_unary_normal, good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal, grad = _grad_broadcast_unary_normal,
inplace = True) inplace = True)
NegTester = make_broadcast_tester(op = neg, NegTester = make_broadcast_restet(op = neg,
expected = lambda x: -x, expected = lambda x: -x,
good = _good_broadcast_unary_normal, good = _good_broadcast_unary_normal,
grad = _grad_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, expected = lambda x: -x,
good = _good_broadcast_unary_normal, good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal, grad = _grad_broadcast_unary_normal,
inplace = True) inplace = True)
SgnTester = make_broadcast_tester(op = sgn, SgnTester = make_broadcast_restet(op = sgn,
expected = numpy.sign, expected = numpy.sign,
good = _good_broadcast_unary_normal) good = _good_broadcast_unary_normal)
SgnInplaceTester = make_broadcast_tester(op = tensor._sgn_inplace, SgnInplaceTester = make_broadcast_restet(op = tensor._sgn_inplace,
expected = numpy.sign, expected = numpy.sign,
good = _good_broadcast_unary_normal, good = _good_broadcast_unary_normal,
inplace = True) inplace = True)
SqrTester = make_broadcast_tester(op = sqr, SqrTester = make_broadcast_restet(op = sqr,
expected = numpy.square, expected = numpy.square,
good = _good_broadcast_unary_normal, good = _good_broadcast_unary_normal,
grad = _grad_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, expected = numpy.square,
good = _good_broadcast_unary_normal, good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal, grad = _grad_broadcast_unary_normal,
inplace = True) inplace = True)
ExpTester = make_broadcast_tester(op = exp, ExpTester = make_broadcast_restet(op = exp,
expected = numpy.exp, expected = numpy.exp,
good = _good_broadcast_unary_normal, good = _good_broadcast_unary_normal,
grad = _grad_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, expected = numpy.exp,
good = _good_broadcast_unary_normal, good = _good_broadcast_unary_normal,
grad = _grad_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)),), ...@@ -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)),)) _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, expected = numpy.log,
good = _good_broadcast_unary_positive, good = _good_broadcast_unary_positive,
grad = _grad_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, expected = numpy.log,
good = _good_broadcast_unary_positive, good = _good_broadcast_unary_positive,
grad = _grad_broadcast_unary_positive, grad = _grad_broadcast_unary_positive,
inplace = True) inplace = True)
Log2Tester = make_broadcast_tester(op = log2, Log2Tester = make_broadcast_restet(op = log2,
expected = numpy.log2, expected = numpy.log2,
good = _good_broadcast_unary_positive, good = _good_broadcast_unary_positive,
grad = _grad_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, expected = numpy.log2,
good = _good_broadcast_unary_positive, good = _good_broadcast_unary_positive,
grad = _grad_broadcast_unary_positive, grad = _grad_broadcast_unary_positive,
inplace = True) inplace = True)
SqrtTester = make_broadcast_tester(op = sqrt, SqrtTester = make_broadcast_restet(op = sqrt,
expected = numpy.sqrt, expected = numpy.sqrt,
good = _good_broadcast_unary_positive, good = _good_broadcast_unary_positive,
grad = _grad_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, expected = numpy.sqrt,
good = _good_broadcast_unary_positive, good = _good_broadcast_unary_positive,
grad = _grad_broadcast_unary_positive, grad = _grad_broadcast_unary_positive,
...@@ -456,33 +456,33 @@ _good_broadcast_unary_wide = dict(normal = (rand_ranged(-1000, 1000, (2, 3)),), ...@@ -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)),)) _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, expected = numpy.sin,
good = _good_broadcast_unary_wide, good = _good_broadcast_unary_wide,
grad = _grad_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, expected = numpy.sin,
good = _good_broadcast_unary_wide, good = _good_broadcast_unary_wide,
grad = _grad_broadcast_unary_wide, grad = _grad_broadcast_unary_wide,
inplace = True) inplace = True)
CosTester = make_broadcast_tester(op = cos, CosTester = make_broadcast_restet(op = cos,
expected = numpy.cos, expected = numpy.cos,
good = _good_broadcast_unary_wide, good = _good_broadcast_unary_wide,
grad = _grad_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, expected = numpy.cos,
good = _good_broadcast_unary_wide, good = _good_broadcast_unary_wide,
grad = _grad_broadcast_unary_wide, grad = _grad_broadcast_unary_wide,
inplace = True) inplace = True)
TanTester = make_broadcast_tester(op = tan, TanTester = make_broadcast_restet(op = tan,
expected = numpy.tan, expected = numpy.tan,
good = dict(normal = (rand_ranged(-3.14, 3.14, (2, 3)),), good = dict(normal = (rand_ranged(-3.14, 3.14, (2, 3)),),
shifted = (rand_ranged(3.15, 6.28, (2, 3)),)), shifted = (rand_ranged(3.15, 6.28, (2, 3)),)),
grad = dict(normal = (rand_ranged(-3.14, 3.14, (2, 3)),), grad = dict(normal = (rand_ranged(-3.14, 3.14, (2, 3)),),
shifted = (rand_ranged(3.15, 6.28, (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, expected = numpy.tan,
good = dict(normal = (rand_ranged(-3.14, 3.14, (2, 3)),), good = dict(normal = (rand_ranged(-3.14, 3.14, (2, 3)),),
shifted = (rand_ranged(3.15, 6.28, (2, 3)),)), shifted = (rand_ranged(3.15, 6.28, (2, 3)),)),
...@@ -491,31 +491,31 @@ TanInplaceTester = make_broadcast_tester(op = tensor._tan_inplace, ...@@ -491,31 +491,31 @@ TanInplaceTester = make_broadcast_tester(op = tensor._tan_inplace,
inplace = True) inplace = True)
CoshTester = make_broadcast_tester(op = cosh, CoshTester = make_broadcast_restet(op = cosh,
expected = numpy.cosh, expected = numpy.cosh,
good = _good_broadcast_unary_normal, good = _good_broadcast_unary_normal,
grad = _grad_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, expected = numpy.cosh,
good = _good_broadcast_unary_normal, good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal, grad = _grad_broadcast_unary_normal,
inplace = True) inplace = True)
SinhTester = make_broadcast_tester(op = sinh, SinhTester = make_broadcast_restet(op = sinh,
expected = numpy.sinh, expected = numpy.sinh,
good = _good_broadcast_unary_normal, good = _good_broadcast_unary_normal,
grad = _grad_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, expected = numpy.sinh,
good = _good_broadcast_unary_normal, good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal, grad = _grad_broadcast_unary_normal,
inplace = True) inplace = True)
TanhTester = make_broadcast_tester(op = tanh, TanhTester = make_broadcast_restet(op = tanh,
expected = numpy.tanh, expected = numpy.tanh,
good = _good_broadcast_unary_normal, good = _good_broadcast_unary_normal,
grad = _grad_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, expected = numpy.tanh,
good = _good_broadcast_unary_normal, good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal, grad = _grad_broadcast_unary_normal,
...@@ -523,7 +523,7 @@ TanhInplaceTester = make_broadcast_tester(op = tensor._tanh_inplace, ...@@ -523,7 +523,7 @@ TanhInplaceTester = make_broadcast_tester(op = tensor._tanh_inplace,
DotTester = make_tester(name = 'DotTester', DotTester = make_restet(name = 'DotTester',
op = dot, op = dot,
expected = lambda x, y: numpy.dot(x, y), expected = lambda x, y: numpy.dot(x, y),
checks = {}, 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): ...@@ -15,7 +15,7 @@ def Env(i, o):
e = gof.Env(i, o) e = gof.Env(i, o)
return e return e
class _test_DimShuffle(unittest.TestCase): class test_DimShuffle(unittest.TestCase):
def with_linker(self, linker): def with_linker(self, linker):
for xsh, shuffle, zsh in [((2, 3), (1, 'x', 0), (3, 1, 2)), for xsh, shuffle, zsh in [((2, 3), (1, 'x', 0), (3, 1, 2)),
...@@ -35,7 +35,7 @@ class _test_DimShuffle(unittest.TestCase): ...@@ -35,7 +35,7 @@ class _test_DimShuffle(unittest.TestCase):
self.with_linker(gof.PerformLinker()) self.with_linker(gof.PerformLinker())
class _test_Broadcast(unittest.TestCase): class test_Broadcast(unittest.TestCase):
def with_linker(self, linker): def with_linker(self, linker):
for xsh, ysh in [((3, 5), (3, 5)), for xsh, ysh in [((3, 5), (3, 5)),
...@@ -119,7 +119,7 @@ class _test_Broadcast(unittest.TestCase): ...@@ -119,7 +119,7 @@ class _test_Broadcast(unittest.TestCase):
assert (f(xv) == zv).all() assert (f(xv) == zv).all()
class _test_CAReduce(unittest.TestCase): class test_CAReduce(unittest.TestCase):
def with_linker(self, linker): def with_linker(self, linker):
for xsh, tosum in [((5, 6), None), 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)): ...@@ -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) 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): def test_double_transpose(self):
x, y, z = inputs() x, y, z = inputs()
...@@ -102,7 +102,7 @@ from theano.tensor import * ...@@ -102,7 +102,7 @@ from theano.tensor import *
#from sandbox import pprint #from sandbox import pprint
class _test_greedy_distribute(unittest.TestCase): class test_greedy_distribute(unittest.TestCase):
def test_main(self): def test_main(self):
a, b, c, d, x, y, z = matrices('abcdxyz') a, b, c, d, x, y, z = matrices('abcdxyz')
e = (a/z + b/x) * x * z e = (a/z + b/x) * x * z
...@@ -115,7 +115,7 @@ class _test_greedy_distribute(unittest.TestCase): ...@@ -115,7 +115,7 @@ class _test_greedy_distribute(unittest.TestCase):
class _test_canonize(unittest.TestCase): class test_canonize(unittest.TestCase):
def test_muldiv(self): def test_muldiv(self):
x, y, z = matrices('xyz') x, y, z = matrices('xyz')
......
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