def test_unary_cwise_real_ops_1(self):
real_ops = [
lambda x: math_ops.acosh(1 + math_ops.square(x)),
math_ops.abs,
math_ops.acos,
math_ops.asin,
math_ops.asinh,
math_ops.atan,
math_ops.atanh,
math_ops.bessel_i0e,
math_ops.bessel_i1e,
math_ops.cos,
math_ops.cosh,
math_ops.digamma,
math_ops.erf,
math_ops.erfc,
math_ops.erfinv,
math_ops.exp,
math_ops.expm1,
math_ops.inv,
math_ops.is_finite,
math_ops.is_inf,
math_ops.lgamma,
math_ops.log,
math_ops.log1p,
math_ops.ndtri,
]
self._test_unary_cwise_ops(real_ops, False)
def test_unary_cwise_real_ops_1(self):
real_ops = [
lambda x: math_ops.acosh(1 + math_ops.square(x)),
math_ops.abs,
math_ops.acos,
math_ops.asin,
math_ops.asinh,
math_ops.atan,
math_ops.atanh,
math_ops.bessel_i0e,
math_ops.bessel_i1e,
math_ops.cos,
math_ops.cosh,
math_ops.digamma,
math_ops.erf,
math_ops.erfc,
math_ops.exp,
math_ops.expm1,
math_ops.inv,
math_ops.is_finite,
math_ops.is_inf,
math_ops.lgamma,
math_ops.log,
math_ops.log1p,
]
self._test_unary_cwise_ops(real_ops, False)
def test_unary_cwise_real_ops_1(self):
if test.is_built_with_rocm():
# TODO(rocm):
# This fails on ROCm...see JIRA ticket 236756
self.skipTest("Fails on ROCM")
real_ops = [
lambda x: math_ops.acosh(1 + math_ops.square(x)),
math_ops.abs,
math_ops.acos,
math_ops.asin,
math_ops.asinh,
math_ops.atan,
math_ops.atanh,
math_ops.cos,
math_ops.cosh,
math_ops.digamma,
math_ops.erf,
math_ops.erfc,
math_ops.erfinv,
math_ops.exp,
math_ops.expm1,
math_ops.inv,
math_ops.is_finite,
math_ops.is_inf,
math_ops.lgamma,
math_ops.log,
math_ops.log1p,
math_ops.ndtri,
special_math_ops.bessel_i0e,
special_math_ops.bessel_i1e,
]
self._test_unary_cwise_ops(real_ops, False)
def _generate_unary_cwise_math_cases():
# TODO(rachelim): Consolidate tests with pfor when APIs are somewhat shared.
bitwise_cases = [("Invert", bitwise_ops.invert)]
logical_cases = [("LogicalNot", math_ops.logical_not)]
complex_cases = [
("Angle", math_ops.angle),
("ComplexAbs", math_ops.abs),
("Conj", math_ops.conj),
("Imag", math_ops.imag),
("Real", math_ops.real),
]
real_cases = [
("Abs", math_ops.abs),
("Acos", math_ops.acos),
("Acosh", lambda x: math_ops.acosh(1 + math_ops.square(x))),
("Asin", math_ops.asin),
("Asinh", math_ops.asinh),
("Atan", math_ops.atan),
("Atanh", math_ops.atanh),
("BesselI0e", math_ops.bessel_i0e),
("BesselI1e", math_ops.bessel_i1e),
("Ceil", math_ops.ceil),
("Cos", math_ops.cos),
("Cosh", math_ops.cosh),
("Digamma", math_ops.digamma),
("Elu", nn.elu),
("Erf", math_ops.erf),
("Erfc", math_ops.erfc),
("Exp", math_ops.exp),
("Expm1", math_ops.expm1),
("Floor", math_ops.floor),
("Inv", math_ops.inv),
("IsFinite", math_ops.is_finite),
("IsInf", math_ops.is_inf),
("Lgamma", math_ops.lgamma),
("Log", math_ops.log),
("Log1p", math_ops.log1p),
("Neg", math_ops.negative),
("Reciprocal", math_ops.reciprocal),
("Relu", nn.relu),
("Relu6", nn.relu6),
("Rint", math_ops.rint),
("Round", math_ops.round),
("Rsqrt", math_ops.rsqrt),
("Selu", nn.selu),
("Sigmoid", math_ops.sigmoid),
("Sign", math_ops.sign),
("Sin", math_ops.sin),
("Sinh", math_ops.sinh),
("Softplus", nn.softplus),
("Softsign", nn.softsign),
("Sqrt", math_ops.sqrt),
("Square", math_ops.square),
("Tan", math_ops.tan),
("Tanh", math_ops.tanh),
]
random_input = np.random.rand(3, 5)
complex_component = np.random.rand(3, 5)
random_int = np.random.randint(0, 10, (7, 3, 5))
def bitwise_dataset_factory():
return dataset_ops.Dataset.from_tensor_slices(random_int)
def logical_dataset_factory():
return dataset_ops.Dataset.from_tensor_slices(random_input > 0)
def random_dataset_factory():
return dataset_ops.Dataset.from_tensor_slices(random_input)
def complex_dataset_factory():
return dataset_ops.Dataset.from_tensor_slices(
math_ops.complex(random_input, complex_component))
case_factory_pairs = [
(bitwise_cases, bitwise_dataset_factory),
(logical_cases, logical_dataset_factory),
(complex_cases, complex_dataset_factory),
(real_cases, random_dataset_factory),
]
return [(case[0], case[1], factory)
for cases, factory in case_factory_pairs for case in cases]
def _generate_unary_cwise_math_cases():
# TODO(rachelim): Consolidate tests with pfor when APIs are somewhat shared.
bitwise_cases = [("Invert", bitwise_ops.invert)]
logical_cases = [("LogicalNot", math_ops.logical_not)]
complex_cases = [
("Angle", math_ops.angle),
("ComplexAbs", math_ops.abs),
("Conj", math_ops.conj),
("Imag", math_ops.imag),
("Real", math_ops.real),
]
real_cases = [
("Abs", math_ops.abs),
("Acos", math_ops.acos),
("Acosh", lambda x: math_ops.acosh(1 + math_ops.square(x))),
("Asin", math_ops.asin),
("Asinh", math_ops.asinh),
("Atan", math_ops.atan),
("Atanh", math_ops.atanh),
("BesselI0e", math_ops.bessel_i0e),
("BesselI1e", math_ops.bessel_i1e),
("Ceil", math_ops.ceil),
("Cos", math_ops.cos),
("Cosh", math_ops.cosh),
("Digamma", math_ops.digamma),
("Elu", nn.elu),
("Erf", math_ops.erf),
("Erfc", math_ops.erfc),
("Exp", math_ops.exp),
("Expm1", math_ops.expm1),
("Floor", math_ops.floor),
("Inv", math_ops.inv),
("IsFinite", math_ops.is_finite),
("IsInf", math_ops.is_inf),
("Lgamma", math_ops.lgamma),
("Log", math_ops.log),
("Log1p", math_ops.log1p),
("Neg", math_ops.negative),
("Reciprocal", math_ops.reciprocal),
("Relu", nn.relu),
("Relu6", nn.relu6),
("Rint", math_ops.rint),
("Round", math_ops.round),
("Rsqrt", math_ops.rsqrt),
("Selu", nn.selu),
("Sigmoid", math_ops.sigmoid),
("Sign", math_ops.sign),
("Sin", math_ops.sin),
("Sinh", math_ops.sinh),
("Softplus", nn.softplus),
("Softsign", nn.softsign),
("Sqrt", math_ops.sqrt),
("Square", math_ops.square),
("Tan", math_ops.tan),
("Tanh", math_ops.tanh),
]
random_input = np.random.rand(3, 5)
complex_component = np.random.rand(3, 5)
random_int = np.random.randint(0, 10, (7, 3, 5))
def bitwise_dataset_factory():
return dataset_ops.Dataset.from_tensor_slices(random_int)
def logical_dataset_factory():
return dataset_ops.Dataset.from_tensor_slices(random_input > 0)
def random_dataset_factory():
return dataset_ops.Dataset.from_tensor_slices(random_input)
def complex_dataset_factory():
return dataset_ops.Dataset.from_tensor_slices(
math_ops.complex(random_input, complex_component))
case_factory_pairs = [
(bitwise_cases, bitwise_dataset_factory),
(logical_cases, logical_dataset_factory),
(complex_cases, complex_dataset_factory),
(real_cases, random_dataset_factory),
]
return [(case[0], case[1], factory)
for cases, factory in case_factory_pairs
for case in cases]
def safe_acosh(x):
return math_ops.acosh(1 + math_ops.square(x))
def test_unary_cwise_ops(self):
complex_ops = [
math_ops.angle,
math_ops.imag,
math_ops.complex_abs,
math_ops.real,
math_ops.conj,
]
real_ops = [
lambda x: math_ops.acosh(1 + math_ops.square(x)),
math_ops.abs,
math_ops.acos,
math_ops.asin,
math_ops.asinh,
math_ops.atan,
math_ops.atanh,
math_ops.bessel_i0e,
math_ops.bessel_i1e,
math_ops.cos,
math_ops.cosh,
math_ops.digamma,
math_ops.erf,
math_ops.erfc,
math_ops.exp,
math_ops.expm1,
math_ops.inv,
math_ops.is_finite,
math_ops.is_inf,
math_ops.lgamma,
math_ops.log,
math_ops.log1p,
math_ops.neg,
math_ops.negative,
math_ops.reciprocal,
math_ops.rint,
math_ops.round,
math_ops.rsqrt,
math_ops.sigmoid,
math_ops.sign,
math_ops.sin,
math_ops.sinh,
math_ops.sqrt,
math_ops.square,
math_ops.tan,
math_ops.tanh,
math_ops.tanh,
nn.elu,
nn.relu,
nn.relu6,
nn.selu,
nn.softplus,
nn.softsign,
]
for op in complex_ops + real_ops:
with backprop.GradientTape(persistent=True) as g:
x = random_ops.random_uniform([3, 5])
g.watch(x)
if op in complex_ops:
y = random_ops.random_uniform([3, 5])
g.watch(y)
x = math_ops.complex(x, y)
# pylint: disable=cell-var-from-loop
output_dtypes = []
def loop_fn(i):
with g:
x1 = array_ops.gather(x, i)
y1 = op(x1)
outputs = [op(x), y1]
if y1.dtype == dtypes.float32:
loss = math_ops.reduce_sum(y1 * y1)
else:
loss = None
if loss is not None:
grad = g.gradient(loss, x1)
if grad is not None:
outputs.append(grad)
del output_dtypes[:]
output_dtypes.extend([t.dtype for t in outputs])
return outputs
# pylint: enable=cell-var-from-loop
self._test_loop_fn(loop_fn, 3, loop_fn_dtypes=output_dtypes)
def test_unary_cwise_ops(self):
complex_ops = [
math_ops.angle,
math_ops.imag,
math_ops.complex_abs,
math_ops.real,
math_ops.conj,
]
real_ops = [
lambda x: math_ops.acosh(1 + math_ops.square(x)),
math_ops.abs,
math_ops.acos,
math_ops.asin,
math_ops.asinh,
math_ops.atan,
math_ops.atanh,
math_ops.bessel_i0e,
math_ops.bessel_i1e,
math_ops.cos,
math_ops.cosh,
math_ops.digamma,
math_ops.erf,
math_ops.erfc,
math_ops.exp,
math_ops.expm1,
math_ops.inv,
math_ops.is_finite,
math_ops.is_inf,
math_ops.lgamma,
math_ops.log,
math_ops.log1p,
math_ops.neg,
math_ops.negative,
math_ops.reciprocal,
math_ops.rint,
math_ops.round,
math_ops.rsqrt,
math_ops.sigmoid,
math_ops.sign,
math_ops.sin,
math_ops.sinh,
math_ops.sqrt,
math_ops.square,
math_ops.tan,
math_ops.tanh,
math_ops.tanh,
nn.elu,
nn.relu,
nn.relu6,
nn.selu,
nn.softplus,
nn.softsign,
]
for op in complex_ops + real_ops:
with backprop.GradientTape(persistent=True) as g:
x = random_ops.random_uniform([3, 5])
g.watch(x)
if op in complex_ops:
y = random_ops.random_uniform([3, 5])
g.watch(y)
x = math_ops.complex(x, y)
# pylint: disable=cell-var-from-loop
output_dtypes = []
def loop_fn(i):
with g:
x1 = array_ops.gather(x, i)
y1 = op(x1)
outputs = [op(x), y1]
if y1.dtype == dtypes.float32:
loss = math_ops.reduce_sum(y1 * y1)
else:
loss = None
if loss is not None:
grad = g.gradient(loss, x1)
if grad is not None:
outputs.append(grad)
del output_dtypes[:]
output_dtypes.extend([t.dtype for t in outputs])
return outputs
# pylint: enable=cell-var-from-loop
self._test_loop_fn(loop_fn, 3, loop_fn_dtypes=output_dtypes)