Python tensorflow.python.ops.math_ops.erfc() Examples
The following are 12
code examples of tensorflow.python.ops.math_ops.erfc().
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Example #1
| Source File: special_math.py From lambda-packs with MIT License | 5 votes |
|
def ndtr(x, name="ndtr"):
"""Normal distribution function.
Returns the area under the Gaussian probability density function, integrated
from minus infinity to x:
```
1 / x
ndtr(x) = ---------- | exp(-0.5 t**2) dt
sqrt(2 pi) /-inf
= 0.5 (1 + erf(x / sqrt(2)))
= 0.5 erfc(x / sqrt(2))
```
Args:
x: `Tensor` of type `float32`, `float64`.
name: Python string. A name for the operation (default="ndtr").
Returns:
ndtr: `Tensor` with `dtype=x.dtype`.
Raises:
TypeError: if `x` is not floating-type.
"""
with ops.name_scope(name, values=[x]):
x = ops.convert_to_tensor(x, name="x")
if x.dtype.as_numpy_dtype not in [np.float32, np.float64]:
raise TypeError(
"x.dtype=%s is not handled, see docstring for supported types."
% x.dtype)
return _ndtr(x)
Example #2
| Source File: special_math.py From lambda-packs with MIT License | 5 votes |
|
def _ndtr(x):
"""Implements ndtr core logic."""
half_sqrt_2 = constant_op.constant(
0.5 * math.sqrt(2.), dtype=x.dtype, name="half_sqrt_2")
w = x * half_sqrt_2
z = math_ops.abs(w)
y = array_ops.where(math_ops.less(z, half_sqrt_2),
1. + math_ops.erf(w),
array_ops.where(math_ops.greater(w, 0.),
2. - math_ops.erfc(z),
math_ops.erfc(z)))
return 0.5 * y
Example #3
| Source File: core_test.py From auto-alt-text-lambda-api with MIT License | 5 votes |
|
def setUp(self):
super(CoreUnaryOpsTest, self).setUp()
self.ops = [
('abs', operator.abs, math_ops.abs, core.abs_function),
('neg', operator.neg, math_ops.negative, core.neg),
# TODO(shoyer): add unary + to core TensorFlow
('pos', None, None, None),
('sign', None, math_ops.sign, core.sign),
('reciprocal', None, math_ops.reciprocal, core.reciprocal),
('square', None, math_ops.square, core.square),
('round', None, math_ops.round, core.round_function),
('sqrt', None, math_ops.sqrt, core.sqrt),
('rsqrt', None, math_ops.rsqrt, core.rsqrt),
('log', None, math_ops.log, core.log),
('exp', None, math_ops.exp, core.exp),
('log', None, math_ops.log, core.log),
('ceil', None, math_ops.ceil, core.ceil),
('floor', None, math_ops.floor, core.floor),
('cos', None, math_ops.cos, core.cos),
('sin', None, math_ops.sin, core.sin),
('tan', None, math_ops.tan, core.tan),
('acos', None, math_ops.acos, core.acos),
('asin', None, math_ops.asin, core.asin),
('atan', None, math_ops.atan, core.atan),
('lgamma', None, math_ops.lgamma, core.lgamma),
('digamma', None, math_ops.digamma, core.digamma),
('erf', None, math_ops.erf, core.erf),
('erfc', None, math_ops.erfc, core.erfc),
('lgamma', None, math_ops.lgamma, core.lgamma),
]
total_size = np.prod([v.size for v in self.original_lt.axes.values()])
self.test_lt = core.LabeledTensor(
math_ops.cast(self.original_lt, dtypes.float32) / total_size,
self.original_lt.axes)
Example #4
| Source File: special_math.py From auto-alt-text-lambda-api with MIT License | 5 votes |
|
def ndtr(x, name="ndtr"):
"""Normal distribution function.
Returns the area under the Gaussian probability density function, integrated
from minus infinity to x:
```
1 / x
ndtr(x) = ---------- | exp(-0.5 t^2) dt
sqrt(2 pi) /-inf
= 0.5 (1 + erf(x / sqrt(2)))
= 0.5 erfc(x / sqrt(2))
```
Args:
x: `Tensor` of type `float32`, `float64`.
name: Python string. A name for the operation (default="ndtr").
Returns:
ndtr: `Tensor` with `dtype=x.dtype`.
Raises:
TypeError: if `x` is not floating-type.
"""
with ops.name_scope(name, values=[x]):
x = ops.convert_to_tensor(x, name="x")
if x.dtype.as_numpy_dtype not in [np.float32, np.float64]:
raise TypeError(
"x.dtype=%s is not handled, see docstring for supported types."
% x.dtype)
return _ndtr(x)
Example #5
| Source File: special_math.py From auto-alt-text-lambda-api with MIT License | 5 votes |
|
def _ndtr(x):
"""Implements ndtr core logic."""
half_sqrt_2 = constant_op.constant(
0.5 * math.sqrt(2.), dtype=x.dtype, name="half_sqrt_2")
w = x * half_sqrt_2
z = math_ops.abs(w)
y = array_ops.where(math_ops.less(z, half_sqrt_2),
1. + math_ops.erf(w),
array_ops.where(math_ops.greater(w, 0.),
2. - math_ops.erfc(z),
math_ops.erfc(z)))
return 0.5 * y
Example #6
| Source File: special_math.py From deep_image_model with Apache License 2.0 | 5 votes |
|
def ndtr(x, name="ndtr"):
"""Normal distribution function.
Returns the area under the Gaussian probability density function, integrated
from minus infinity to x:
```
1 / x
ndtr(x) = ---------- | exp(-0.5 t^2) dt
sqrt(2 pi) /-inf
= 0.5 (1 + erf(x / sqrt(2)))
= 0.5 erfc(x / sqrt(2))
```
Args:
x: `Tensor` of type `float32`, `float64`.
name: Python string. A name for the operation (default="ndtr").
Returns:
ndtr: `Tensor` with `dtype=x.dtype`.
Raises:
TypeError: if `x` is not floating-type.
"""
with ops.name_scope(name, values=[x]):
x = ops.convert_to_tensor(x, name="x")
if x.dtype.as_numpy_dtype not in [np.float32, np.float64]:
raise TypeError(
"x.dtype=%s is not handled, see docstring for supported types."
% x.dtype)
return _ndtr(x)
Example #7
| Source File: special_math.py From deep_image_model with Apache License 2.0 | 5 votes |
|
def _ndtr(x):
"""Implements ndtr core logic."""
half_sqrt_2 = constant_op.constant(
0.5 * math.sqrt(2.), dtype=x.dtype, name="half_sqrt_2")
w = x * half_sqrt_2
z = math_ops.abs(w)
y = math_ops.select(math_ops.less(z, half_sqrt_2),
1. + math_ops.erf(w),
math_ops.select(math_ops.greater(w, 0.),
2. - math_ops.erfc(z),
math_ops.erfc(z)))
return 0.5 * y
Example #8
| Source File: special_math.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 5 votes |
|
def ndtr(x, name="ndtr"):
"""Normal distribution function.
Returns the area under the Gaussian probability density function, integrated
from minus infinity to x:
```
1 / x
ndtr(x) = ---------- | exp(-0.5 t**2) dt
sqrt(2 pi) /-inf
= 0.5 (1 + erf(x / sqrt(2)))
= 0.5 erfc(x / sqrt(2))
```
Args:
x: `Tensor` of type `float32`, `float64`.
name: Python string. A name for the operation (default="ndtr").
Returns:
ndtr: `Tensor` with `dtype=x.dtype`.
Raises:
TypeError: if `x` is not floating-type.
"""
with ops.name_scope(name, values=[x]):
x = ops.convert_to_tensor(x, name="x")
if x.dtype.as_numpy_dtype not in [np.float32, np.float64]:
raise TypeError(
"x.dtype=%s is not handled, see docstring for supported types."
% x.dtype)
return _ndtr(x)
Example #9
| Source File: special_math.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 5 votes |
|
def _ndtr(x):
"""Implements ndtr core logic."""
half_sqrt_2 = constant_op.constant(
0.5 * math.sqrt(2.), dtype=x.dtype, name="half_sqrt_2")
w = x * half_sqrt_2
z = math_ops.abs(w)
y = array_ops.where(math_ops.less(z, half_sqrt_2),
1. + math_ops.erf(w),
array_ops.where(math_ops.greater(w, 0.),
2. - math_ops.erfc(z),
math_ops.erfc(z)))
return 0.5 * y
Example #10
| Source File: core_test.py From keras-lambda with MIT License | 5 votes |
|
def setUp(self):
super(CoreUnaryOpsTest, self).setUp()
self.ops = [
('abs', operator.abs, math_ops.abs, core.abs_function),
('neg', operator.neg, math_ops.negative, core.neg),
# TODO(shoyer): add unary + to core TensorFlow
('pos', None, None, None),
('sign', None, math_ops.sign, core.sign),
('reciprocal', None, math_ops.reciprocal, core.reciprocal),
('square', None, math_ops.square, core.square),
('round', None, math_ops.round, core.round_function),
('sqrt', None, math_ops.sqrt, core.sqrt),
('rsqrt', None, math_ops.rsqrt, core.rsqrt),
('log', None, math_ops.log, core.log),
('exp', None, math_ops.exp, core.exp),
('log', None, math_ops.log, core.log),
('ceil', None, math_ops.ceil, core.ceil),
('floor', None, math_ops.floor, core.floor),
('cos', None, math_ops.cos, core.cos),
('sin', None, math_ops.sin, core.sin),
('tan', None, math_ops.tan, core.tan),
('acos', None, math_ops.acos, core.acos),
('asin', None, math_ops.asin, core.asin),
('atan', None, math_ops.atan, core.atan),
('lgamma', None, math_ops.lgamma, core.lgamma),
('digamma', None, math_ops.digamma, core.digamma),
('erf', None, math_ops.erf, core.erf),
('erfc', None, math_ops.erfc, core.erfc),
('lgamma', None, math_ops.lgamma, core.lgamma),
]
total_size = np.prod([v.size for v in self.original_lt.axes.values()])
self.test_lt = core.LabeledTensor(
math_ops.cast(self.original_lt, dtypes.float32) / total_size,
self.original_lt.axes)
Example #11
| Source File: special_math.py From keras-lambda with MIT License | 5 votes |
|
def ndtr(x, name="ndtr"):
"""Normal distribution function.
Returns the area under the Gaussian probability density function, integrated
from minus infinity to x:
```
1 / x
ndtr(x) = ---------- | exp(-0.5 t^2) dt
sqrt(2 pi) /-inf
= 0.5 (1 + erf(x / sqrt(2)))
= 0.5 erfc(x / sqrt(2))
```
Args:
x: `Tensor` of type `float32`, `float64`.
name: Python string. A name for the operation (default="ndtr").
Returns:
ndtr: `Tensor` with `dtype=x.dtype`.
Raises:
TypeError: if `x` is not floating-type.
"""
with ops.name_scope(name, values=[x]):
x = ops.convert_to_tensor(x, name="x")
if x.dtype.as_numpy_dtype not in [np.float32, np.float64]:
raise TypeError(
"x.dtype=%s is not handled, see docstring for supported types."
% x.dtype)
return _ndtr(x)
Example #12
| Source File: special_math.py From keras-lambda with MIT License | 5 votes |
|
def _ndtr(x):
"""Implements ndtr core logic."""
half_sqrt_2 = constant_op.constant(
0.5 * math.sqrt(2.), dtype=x.dtype, name="half_sqrt_2")
w = x * half_sqrt_2
z = math_ops.abs(w)
y = array_ops.where(math_ops.less(z, half_sqrt_2),
1. + math_ops.erf(w),
array_ops.where(math_ops.greater(w, 0.),
2. - math_ops.erfc(z),
math_ops.erfc(z)))
return 0.5 * y
