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pytensor
提交 8aefb2f5 authored 作者: Nicolas Bouchard's avatar Nicolas Bouchard
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上级 4b909254
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正在显示 包含 270 行增加 和 289 行删除
theano/scalar/basic.py
浏览文件 @ 8aefb2f5
......@@ -2018,7 +2018,7 @@ class Cos(UnaryScalarOp):
cos = Cos(upgrade_to_float, name='cos')
class Arccos(UnaryScalarOp):
class ArcCos(UnaryScalarOp):
def impl(self, x):
return numpy.arccos(x)
......@@ -2034,7 +2034,7 @@ class Arccos(UnaryScalarOp):
if node.inputs[0].type in complex_types:
raise NotImplementedError('type not supported', type)
return "%(z)s = acos(%(x)s);" % locals()
arccos = Arccos(upgrade_to_float, name='arccos')
arccos = ArcCos(upgrade_to_float, name='arccos')
class Sin(UnaryScalarOp):
......@@ -2056,7 +2056,7 @@ class Sin(UnaryScalarOp):
sin = Sin(upgrade_to_float, name='sin')
class Arcsin(UnaryScalarOp):
class ArcSin(UnaryScalarOp):
def impl(self, x):
return numpy.arcsin(x)
......@@ -2072,7 +2072,7 @@ class Arcsin(UnaryScalarOp):
if node.inputs[0].type in complex_types:
raise NotImplementedError('type not supported', type)
return "%(z)s = asin(%(x)s);" % locals()
arcsin = Arcsin(upgrade_to_float, name='arcsin')
arcsin = ArcSin(upgrade_to_float, name='arcsin')
class Tan(UnaryScalarOp):
......@@ -2094,7 +2094,7 @@ class Tan(UnaryScalarOp):
tan = Tan(upgrade_to_float, name='tan')
class Arctan(UnaryScalarOp):
class ArcTan(UnaryScalarOp):
def impl(self, x):
return numpy.arctan(x)
......@@ -2110,7 +2110,7 @@ class Arctan(UnaryScalarOp):
if node.inputs[0].type in complex_types:
raise NotImplementedError('type not supported', type)
return "%(z)s = atan(%(x)s);" % locals()
arctan = Arctan(upgrade_to_float, name='arctan')
arctan = ArcTan(upgrade_to_float, name='arctan')
class Cosh(UnaryScalarOp):
......
theano/tensor/tests/test_basic.py
浏览文件 @ 8aefb2f5
......@@ -908,304 +908,285 @@ FloorInplaceTester = makeBroadcastTester(op=inplace.floor_inplace,
expected=lambda a: numpy.asarray(numpy.floor(a), a.dtype),
good=_good_broadcast_unary_normal_no_complex,
grad=_grad_broadcast_unary_normal,
inplace = True)
inplace=True)
RoundHalfToEvenTester = makeBroadcastTester(op = tensor.round_half_to_even,
expected = numpy.round,
good = _good_broadcast_unary_normal_float_no_complex)
RoundHalfToEvenTester = makeBroadcastTester(
op=tensor.round_half_to_even,
expected=numpy.round,
good=_good_broadcast_unary_normal_float_no_complex)
# TODO: Why complex are accepted in the next one?
RoundHalfToEvenInplaceTester = makeBroadcastTester(op = inplace.round_half_to_even_inplace,
expected = numpy.round,
good = _good_broadcast_unary_normal_float,
inplace = True)
RoundHalfToEvenInplaceTester = makeBroadcastTester(
op=inplace.round_half_to_even_inplace,
expected=numpy.round,
good=_good_broadcast_unary_normal_float,
inplace=True)
#numpy.vectorize don't handle correctly empty ndarray.
#see in their file numpy/lib/function_base.py in class vectorize.__call__
#This happen in float32 mode.
RoundHalfAwayFromZeroTester = makeBroadcastTester(op = tensor.round_half_away_from_zero,
expected = theano.scalar.basic.round_half_away_from_zero_vec,
good = _good_broadcast_unary_normal_float_no_empty_no_complex)#_good_broadcast_unary_normal_float)
RoundHalfAwayFromZeroInplaceTester = makeBroadcastTester(op = inplace.round_half_away_from_zero_inplace,
expected = theano.scalar.basic.round_half_away_from_zero_vec,
good = _good_broadcast_unary_normal_float_no_empty_no_complex,
inplace = True)
SqrTester = makeBroadcastTester(op = tensor.sqr,
expected = numpy.square,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal)
SqrInplaceTester = makeBroadcastTester(op = inplace.sqr_inplace,
expected = numpy.square,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal,
inplace = True)
ExpTester = makeBroadcastTester(op = tensor.exp,
expected = numpy.exp,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal)
ExpInplaceTester = makeBroadcastTester(op = inplace.exp_inplace,
expected = numpy.exp,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal,
inplace = True)
Exp2Tester = makeBroadcastTester(op = tensor.exp2,
expected = numpy.exp2,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal)
Exp2InplaceTester = makeBroadcastTester(op = inplace.exp2_inplace,
expected = numpy.exp2,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal,
inplace = True)
_good_broadcast_unary_positive = dict(normal = (rand_ranged(0.001, 5, (2, 3)),),
integers = (randint_ranged(1, 5, (2, 3)),),
complex = (randc128_ranged(1, 5, (2,3)),),
empty = (numpy.asarray([]),),
)
RoundHalfAwayFromZeroTester = makeBroadcastTester(
op=tensor.round_half_away_from_zero,
expected=theano.scalar.basic.round_half_away_from_zero_vec,
good=_good_broadcast_unary_normal_float_no_empty_no_complex)
#_good_broadcast_unary_normal_float)
RoundHalfAwayFromZeroInplaceTester = makeBroadcastTester(
op=inplace.round_half_away_from_zero_inplace,
expected=theano.scalar.basic.round_half_away_from_zero_vec,
good=_good_broadcast_unary_normal_float_no_empty_no_complex,
inplace=True)
_grad_broadcast_unary_positive = dict(normal = (rand_ranged(0.001, 5, (2, 3)),),
#complex = (randc128_ranged(1, 5, (2,3)),),
#empty = (numpy.asarray([]),),
SqrTester = makeBroadcastTester(op=tensor.sqr,
expected=numpy.square,
good=_good_broadcast_unary_normal,
grad=_grad_broadcast_unary_normal)
SqrInplaceTester = makeBroadcastTester(op=inplace.sqr_inplace,
expected=numpy.square,
good=_good_broadcast_unary_normal,
grad=_grad_broadcast_unary_normal,
inplace=True)
ExpTester = makeBroadcastTester(op=tensor.exp,
expected=numpy.exp,
good=_good_broadcast_unary_normal,
grad=_grad_broadcast_unary_normal)
ExpInplaceTester = makeBroadcastTester(op=inplace.exp_inplace,
expected=numpy.exp,
good=_good_broadcast_unary_normal,
grad=_grad_broadcast_unary_normal,
inplace=True)
Exp2Tester = makeBroadcastTester(op=tensor.exp2,
expected=numpy.exp2,
good=_good_broadcast_unary_normal,
grad=_grad_broadcast_unary_normal)
Exp2InplaceTester = makeBroadcastTester(op=inplace.exp2_inplace,
expected=numpy.exp2,
good=_good_broadcast_unary_normal,
grad=_grad_broadcast_unary_normal,
inplace=True)
_good_broadcast_unary_positive = dict(normal=(rand_ranged(0.001, 5, (2, 3)),),
integers=(randint_ranged(1, 5, (2, 3)),),
complex=(randc128_ranged(1, 5, (2, 3)),),
empty=(numpy.asarray([]),),
)
LogTester = makeBroadcastTester(op = tensor.log,
expected = numpy.log,
good = _good_broadcast_unary_positive,
grad = _grad_broadcast_unary_positive)
LogInplaceTester = makeBroadcastTester(op = inplace.log_inplace,
expected = numpy.log,
good = _good_broadcast_unary_positive,
grad = _grad_broadcast_unary_positive,
inplace = True)
Log2Tester = makeBroadcastTester(op = tensor.log2,
expected = numpy.log2,
good = _good_broadcast_unary_positive,
grad = _grad_broadcast_unary_positive)
Log2InplaceTester = makeBroadcastTester(op = inplace.log2_inplace,
expected = numpy.log2,
good = _good_broadcast_unary_positive,
grad = _grad_broadcast_unary_positive,
inplace = True)
Log10Tester = makeBroadcastTester(op = tensor.log10,
expected = numpy.log10,
good = _good_broadcast_unary_positive,
grad = _grad_broadcast_unary_positive)
Log10InplaceTester = makeBroadcastTester(op = inplace.log10_inplace,
expected = numpy.log10,
good = _good_broadcast_unary_positive,
grad = _grad_broadcast_unary_positive,
inplace = True)
Log1pTester = makeBroadcastTester(op = tensor.log1p,
expected = numpy.log1p,
good = _good_broadcast_unary_positive,
grad = _grad_broadcast_unary_positive)
Log1pInplaceTester = makeBroadcastTester(op = inplace.log1p_inplace,
expected = numpy.log1p,
good = _good_broadcast_unary_positive,
grad = _grad_broadcast_unary_positive,
inplace = True)
SqrtTester = makeBroadcastTester(op = tensor.sqrt,
expected = numpy.sqrt,
good = _good_broadcast_unary_positive,
grad = _grad_broadcast_unary_positive)
SqrtInplaceTester = makeBroadcastTester(op = inplace.sqrt_inplace,
expected = numpy.sqrt,
good = _good_broadcast_unary_positive,
grad = _grad_broadcast_unary_positive,
inplace = True)
_good_broadcast_unary_wide = dict(normal = (rand_ranged(-1000, 1000, (2, 3)),),
integers = (randint_ranged(-1000, 1000, (2, 3)),),
complex = (randc128_ranged(-1000, 1000, (2, 3)),),
empty = (numpy.asarray([]),),)
_grad_broadcast_unary_wide = dict(normal = (rand_ranged(-1000, 1000, (2, 3)),),
#complex = (randc128_ranged(-1000, 1000, (2, 3)),),
#empty = (numpy.asarray([]),),
)
_good_broadcast_unary_arccos = dict(normal = (rand_ranged(-1.+1e-7, 1.-1e-7, (2, 3)),),
integers = (randint_ranged(-1.+1e-7, 1-1e-7, (2, 3)),),
complex = (randc128_ranged(-1.+1e-7, 1-1e-7, (2, 3)),),
empty = (numpy.asarray([]),),)
_grad_broadcast_unary_arccos = dict(normal = (rand_ranged(-1.+1e-7, 1-1e-7, (2, 3)),),
#complex = (randc128_ranged(-1000, 1000, (2, 3)),),
#empty = (numpy.asarray([]),),
)
_good_broadcast_unary_arcsin = _good_broadcast_unary_arccos
_grad_broadcast_unary_arcsin = _grad_broadcast_unary_arccos
SinTester = makeBroadcastTester(op = tensor.sin,
expected = numpy.sin,
good = _good_broadcast_unary_wide,
grad = _grad_broadcast_unary_wide)
SinInplaceTester = makeBroadcastTester(op = inplace.sin_inplace,
expected = numpy.sin,
good = _good_broadcast_unary_wide,
grad = _grad_broadcast_unary_wide,
inplace = True)
ArcsinTester = makeBroadcastTester(op = tensor.arcsin,
expected = numpy.arcsin,
good = _good_broadcast_unary_arcsin,
grad = _grad_broadcast_unary_arcsin)
ArcsinInplaceTester = makeBroadcastTester(op = inplace.arcsin_inplace,
expected = numpy.arcsin,
good = _good_broadcast_unary_arcsin,
grad = _grad_broadcast_unary_arcsin,
inplace = True)
CosTester = makeBroadcastTester(op = tensor.cos,
expected = numpy.cos,
good = _good_broadcast_unary_wide,
grad = _grad_broadcast_unary_wide)
CosInplaceTester = makeBroadcastTester(op = inplace.cos_inplace,
expected = numpy.cos,
good = _good_broadcast_unary_wide,
grad = _grad_broadcast_unary_wide,
inplace = True)
ArccosTester = makeBroadcastTester(op = tensor.arccos,
expected = numpy.arccos,
good = _good_broadcast_unary_arccos,
grad = _grad_broadcast_unary_arccos)
ArccosInplaceTester = makeBroadcastTester(op = inplace.arccos_inplace,
expected = numpy.arccos,
good = _good_broadcast_unary_arccos,
grad = _grad_broadcast_unary_arccos,
inplace = True)
_grad_broadcast_unary_positive = dict(normal=(rand_ranged(0.001, 5, (2, 3)),),)
LogTester = makeBroadcastTester(op=tensor.log,
expected=numpy.log,
good=_good_broadcast_unary_positive,
grad=_grad_broadcast_unary_positive)
LogInplaceTester = makeBroadcastTester(op=inplace.log_inplace,
expected=numpy.log,
good=_good_broadcast_unary_positive,
grad=_grad_broadcast_unary_positive,
inplace=True)
Log2Tester = makeBroadcastTester(op=tensor.log2,
expected=numpy.log2,
good=_good_broadcast_unary_positive,
grad=_grad_broadcast_unary_positive)
Log2InplaceTester = makeBroadcastTester(op=inplace.log2_inplace,
expected=numpy.log2,
good=_good_broadcast_unary_positive,
grad=_grad_broadcast_unary_positive,
inplace=True)
Log10Tester = makeBroadcastTester(op=tensor.log10,
expected=numpy.log10,
good=_good_broadcast_unary_positive,
grad=_grad_broadcast_unary_positive)
Log10InplaceTester = makeBroadcastTester(op=inplace.log10_inplace,
expected=numpy.log10,
good=_good_broadcast_unary_positive,
grad=_grad_broadcast_unary_positive,
inplace=True)
Log1pTester = makeBroadcastTester(op=tensor.log1p,
expected=numpy.log1p,
good=_good_broadcast_unary_positive,
grad=_grad_broadcast_unary_positive)
Log1pInplaceTester = makeBroadcastTester(op=inplace.log1p_inplace,
expected=numpy.log1p,
good=_good_broadcast_unary_positive,
grad=_grad_broadcast_unary_positive,
inplace=True)
SqrtTester = makeBroadcastTester(op=tensor.sqrt,
expected=numpy.sqrt,
good=_good_broadcast_unary_positive,
grad=_grad_broadcast_unary_positive)
SqrtInplaceTester = makeBroadcastTester(op=inplace.sqrt_inplace,
expected=numpy.sqrt,
good=_good_broadcast_unary_positive,
grad=_grad_broadcast_unary_positive,
inplace=True)
_good_broadcast_unary_wide = dict(
normal=(rand_ranged(-1000, 1000, (2, 3)),),
integers=(randint_ranged(-1000, 1000, (2, 3)),),
complex=(randc128_ranged(-1000, 1000, (2, 3)),),
empty=(numpy.asarray([]),),)
_grad_broadcast_unary_wide = dict(normal=(rand_ranged(-1000, 1000, (2, 3)),),)
SinTester = makeBroadcastTester(op=tensor.sin,
expected=numpy.sin,
good=_good_broadcast_unary_wide,
grad=_grad_broadcast_unary_wide)
SinInplaceTester = makeBroadcastTester(op=inplace.sin_inplace,
expected=numpy.sin,
good=_good_broadcast_unary_wide,
grad=_grad_broadcast_unary_wide,
inplace=True)
_good_broadcast_unary_arcsin = dict(normal=(rand_ranged(-1, 1, (2, 3)),),
integers=(randint_ranged(-1, 1, (2, 3)),),
complex=(randc128_ranged(-1, 1, (2, 3)),),
empty=(numpy.asarray([]),),)
_grad_broadcast_unary_arcsin = dict(normal=(rand_ranged(-1, 1, (2, 3)),),)
ArcSinTester = makeBroadcastTester(op=tensor.arcsin,
expected=numpy.arcsin,
good=_good_broadcast_unary_arcsin,
grad=_grad_broadcast_unary_arcsin)
ArcSinInplaceTester = makeBroadcastTester(op=inplace.arcsin_inplace,
expected=numpy.arcsin,
good=_good_broadcast_unary_arcsin,
grad=_grad_broadcast_unary_arcsin,
inplace=True)
CosTester = makeBroadcastTester(op=tensor.cos,
expected=numpy.cos,
good=_good_broadcast_unary_wide,
grad=_grad_broadcast_unary_wide)
CosInplaceTester = makeBroadcastTester(op=inplace.cos_inplace,
expected=numpy.cos,
good=_good_broadcast_unary_wide,
grad=_grad_broadcast_unary_wide,
inplace=True)
ArcCosTester = makeBroadcastTester(op=tensor.arccos,
expected=numpy.arccos,
good=_good_broadcast_unary_arcsin,
grad=_grad_broadcast_unary_arcsin)
ArcCosInplaceTester = makeBroadcastTester(op=inplace.arccos_inplace,
expected=numpy.arccos,
good=_good_broadcast_unary_arcsin,
grad=_grad_broadcast_unary_arcsin,
inplace=True)
tan_grad_rtol = None
if config.floatX=='float32':
#We raise the relative tolerence for the grad as their is error in float32
#This is probably caused by our way of computing the gradient error.
if config.floatX == 'float32':
#We raise the relative tolerence for the grad as their is error in float32
#This is probably caused by our way of computing the gradient error.
tan_grad_rtol = 0.052
TanTester = makeBroadcastTester(op = tensor.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)),)),
grad_rtol=tan_grad_rtol)
TanInplaceTester = makeBroadcastTester(op = inplace.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)),)),
grad = dict(normal = (rand_ranged(-3.14, 3.14, (2, 3)),),
shifted = (rand_ranged(3.15, 6.28, (2, 3)),)),
grad_rtol=tan_grad_rtol,
inplace = True)
ArctanTester = makeBroadcastTester(op = tensor.tan,
expected = numpy.tan,
good = _good_broadcast_unary_wide,
grad = _grad_broadcast_unary_wide,
_good_broadcast_unary_tan = dict(
normal=(rand_ranged(-3.14, 3.14, (2, 3)),),
shifted=(rand_ranged(3.15, 6.28, (2, 3)),),
integers=(randint_ranged(-3, 3, (2, 3)),),
complex=(randc128_ranged(-3.14, 3.14, (2, 3)),),
empty=(numpy.asarray([]),),)
_grad_broadcast_unary_tan = dict(normal=(rand_ranged(-3.14, 3.14, (2, 3)),),
shifted=(rand_ranged(3.15, 6.28, (2, 3)),))
TanTester = makeBroadcastTester(op=tensor.tan,
expected=numpy.tan,
good=_good_broadcast_unary_tan,
grad=_grad_broadcast_unary_tan,
grad_rtol=tan_grad_rtol)
TanInplaceTester = makeBroadcastTester(op=inplace.tan_inplace,
expected=numpy.tan,
good=_good_broadcast_unary_tan,
grad=_grad_broadcast_unary_tan,
grad_rtol=tan_grad_rtol,
inplace=True)
ArcTanTester = makeBroadcastTester(op=tensor.tan,
expected=numpy.tan,
good=_good_broadcast_unary_wide,
grad=_grad_broadcast_unary_wide,
grad_rtol=tan_grad_rtol)
ArctanInplaceTester = makeBroadcastTester(op = inplace.tan_inplace,
expected = numpy.tan,
good = _good_broadcast_unary_wide,
grad = _grad_broadcast_unary_wide,
ArcTanInplaceTester = makeBroadcastTester(op=inplace.tan_inplace,
expected=numpy.tan,
good=_good_broadcast_unary_wide,
grad=_grad_broadcast_unary_wide,
grad_rtol=tan_grad_rtol,
inplace = True)
CoshTester = makeBroadcastTester(op = tensor.cosh,
expected = numpy.cosh,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal)
CoshInplaceTester = makeBroadcastTester(op = inplace.cosh_inplace,
expected = numpy.cosh,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal,
inplace = True)
_good_broadcast_unary_arccosh = dict(normal = (rand_ranged(1, 1000, (2, 3)),),
integers = (randint_ranged(1, 1000, (2, 3)),),
complex = (randc128_ranged(1, 1000, (2, 3)),),
empty = (numpy.asarray([]),),)
_grad_broadcast_unary_arccosh = dict(normal = (rand_ranged(1, 1000, (2, 3)),),)
ArccoshTester = makeBroadcastTester(op = tensor.arccosh,
expected = numpy.arccosh,
good = _good_broadcast_unary_arccosh,
grad = _grad_broadcast_unary_arccosh)
ArccoshInplaceTester = makeBroadcastTester(op = inplace.arccosh_inplace,
expected = numpy.arccosh,
good = _good_broadcast_unary_arccosh,
grad = _grad_broadcast_unary_arccosh,
inplace = True)
SinhTester = makeBroadcastTester(op = tensor.sinh,
expected = numpy.sinh,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal)
SinhInplaceTester = makeBroadcastTester(op = inplace.sinh_inplace,
expected = numpy.sinh,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal,
inplace = True)
ArcsinhTester = makeBroadcastTester(op = tensor.arcsinh,
expected = numpy.arcsinh,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal)
ArcsinhInplaceTester = makeBroadcastTester(op = inplace.arcsinh_inplace,
expected = numpy.arcsinh,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal,
inplace = True)
TanhTester = makeBroadcastTester(op = tensor.tanh,
expected = numpy.tanh,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal)
TanhInplaceTester = makeBroadcastTester(op = inplace.tanh_inplace,
expected = numpy.tanh,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal,
inplace = True)
_good_broadcast_unary_arctanh = dict(normal = (rand_ranged(-1, 1, (2, 3)),),
integers = (randint_ranged(-1, 1, (2, 3)),),
complex = (randc128_ranged(-1, 1, (2, 3)),),
empty = (numpy.asarray([]),),)
_grad_broadcast_unary_arctanh = dict(normal = (rand_ranged(-1, 1, (2, 3)),),)
ArctanhTester = makeBroadcastTester(op = tensor.arctanh,
expected = numpy.arctanh,
good = _good_broadcast_unary_arctanh,
grad = _grad_broadcast_unary_arctanh)
ArctanhInplaceTester = makeBroadcastTester(op = inplace.arctanh_inplace,
expected = numpy.arctanh,
good = _good_broadcast_unary_arctanh,
grad = _grad_broadcast_unary_arctanh,
inplace = True)
inplace=True)
CoshTester = makeBroadcastTester(op=tensor.cosh,
expected=numpy.cosh,
good=_good_broadcast_unary_normal,
grad=_grad_broadcast_unary_normal)
CoshInplaceTester = makeBroadcastTester(op=inplace.cosh_inplace,
expected=numpy.cosh,
good=_good_broadcast_unary_normal,
grad=_grad_broadcast_unary_normal,
inplace=True)
_good_broadcast_unary_arccosh = dict(
normal=(rand_ranged(1, 1000, (2, 3)),),
integers=(randint_ranged(1, 1000, (2, 3)),),
complex=(randc128_ranged(1, 1000, (2, 3)),),
empty=(numpy.asarray([]),),)
_grad_broadcast_unary_arccosh = dict(normal=(rand_ranged(1, 1000, (2, 3)),),)
ArcCoshTester = makeBroadcastTester(op=tensor.arccosh,
expected=numpy.arccosh,
good=_good_broadcast_unary_arccosh,
grad=_grad_broadcast_unary_arccosh)
ArcCoshInplaceTester = makeBroadcastTester(op=inplace.arccosh_inplace,
expected=numpy.arccosh,
good=_good_broadcast_unary_arccosh,
grad=_grad_broadcast_unary_arccosh,
inplace=True)
SinhTester = makeBroadcastTester(op=tensor.sinh,
expected=numpy.sinh,
good=_good_broadcast_unary_normal,
grad=_grad_broadcast_unary_normal)
SinhInplaceTester = makeBroadcastTester(op=inplace.sinh_inplace,
expected=numpy.sinh,
good=_good_broadcast_unary_normal,
grad=_grad_broadcast_unary_normal,
inplace=True)
ArcSinhTester = makeBroadcastTester(op=tensor.arcsinh,
expected=numpy.arcsinh,
good=_good_broadcast_unary_normal,
grad=_grad_broadcast_unary_normal)
ArcSinhInplaceTester = makeBroadcastTester(op=inplace.arcsinh_inplace,
expected=numpy.arcsinh,
good=_good_broadcast_unary_normal,
grad=_grad_broadcast_unary_normal,
inplace=True)
TanhTester = makeBroadcastTester(op=tensor.tanh,
expected=numpy.tanh,
good=_good_broadcast_unary_normal,
grad=_grad_broadcast_unary_normal)
TanhInplaceTester = makeBroadcastTester(op=inplace.tanh_inplace,
expected=numpy.tanh,
good=_good_broadcast_unary_normal,
grad=_grad_broadcast_unary_normal,
inplace=True)
_good_broadcast_unary_arctanh = dict(normal=(rand_ranged(-1, 1, (2, 3)),),
integers=(randint_ranged(-1, 1, (2, 3)),),
complex=(randc128_ranged(-1, 1, (2, 3)),),
empty=(numpy.asarray([]),),)
_grad_broadcast_unary_arctanh = dict(normal=(rand_ranged(-1, 1, (2, 3)),),)
ArcTanhTester = makeBroadcastTester(op=tensor.arctanh,
expected=numpy.arctanh,
good=_good_broadcast_unary_arctanh,
grad=_grad_broadcast_unary_arctanh)
ArcTanhInplaceTester = makeBroadcastTester(op=inplace.arctanh_inplace,
expected=numpy.arctanh,
good=_good_broadcast_unary_arctanh,
grad=_grad_broadcast_unary_arctanh,
inplace=True)
#inplace ops when the input is integer and the output is float*
......
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