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TensorFlow.NET/src/TensorFlowNET.Core/APIs/tf.math.cs at master · pathway27/TensorFlow.NET · GitHub
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/*****************************************************************************
Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
******************************************************************************/
using
Tensorflow
.
Eager
;
using
Tensorflow
.
Operations
;
namespace
Tensorflow
{
public
partial
class
tensorflow
{
public
MathApi
math
{
get
;
}
=
new
MathApi
(
)
;
public
class
MathApi
{
public
Tensor
log
(
Tensor
x
,
string
name
=
null
)
=>
gen_math_ops
.
log
(
x
,
name
)
;
}
public
Tensor
abs
(
Tensor
x
,
string
name
=
null
)
=>
math_ops
.
abs
(
x
,
name
)
;
/// <summary>
/// Computes acos of x element-wise.
/// </summary>
/// <param name="x"></param>
/// <param name="name"></param>
/// <returns></returns>
public
Tensor
acos
(
Tensor
x
,
string
name
=
null
)
=>
gen_math_ops
.
acos
(
x
,
name
)
;
/// <summary>
/// Computes asin of x element-wise.
/// </summary>
/// <param name="x"></param>
/// <param name="name"></param>
/// <returns></returns>
public
Tensor
asin
(
Tensor
x
,
string
name
=
null
)
=>
gen_math_ops
.
asin
(
x
,
name
)
;
public
Tensor
add
(
Tensor
a
,
Tensor
b
,
string
name
=
null
)
=>
gen_math_ops
.
add
(
a
,
b
,
name
:
name
)
;
public
Tensor
add
<
Tx
,
Ty
>
(
Tx
a
,
Ty
b
,
string
name
=
null
)
=>
gen_math_ops
.
add
(
a
,
b
,
name
:
name
)
;
/// <summary>
/// Adds all input tensors element-wise.
/// </summary>
/// <param name="inputs"></param>
/// <param name="name"></param>
/// <returns>A `Tensor` of same shape and type as the elements of `inputs`.</returns>
public
Tensor
add_n
(
Tensor
[
]
inputs
,
string
name
=
null
)
=>
math_ops
.
add_n
(
inputs
,
name
:
name
)
;
/// <summary>
/// Computes atan of x element-wise.
/// </summary>
/// <param name="x"></param>
/// <param name="name"></param>
/// <returns></returns>
public
Tensor
atan
(
Tensor
x
,
string
name
=
null
)
=>
gen_math_ops
.
atan
(
x
,
name
)
;
public
Tensor
arg_max
(
Tensor
input
,
int
dimension
,
TF_DataType
output_type
=
TF_DataType
.
TF_INT64
,
string
name
=
null
)
=>
gen_math_ops
.
arg_max
(
input
,
dimension
,
output_type
:
output_type
,
name
:
name
)
;
public
Tensor
arg_min
(
Tensor
input
,
int
dimension
,
TF_DataType
output_type
=
TF_DataType
.
TF_INT64
,
string
name
=
null
)
=>
gen_math_ops
.
arg_min
(
input
,
dimension
,
output_type
:
output_type
,
name
:
name
)
;
public
Tensor
is_finite
(
Tensor
input
,
string
name
=
null
)
=>
gen_math_ops
.
is_finite
(
input
,
name
)
;
public
Tensor
is_nan
(
Tensor
input
,
string
name
=
null
)
=>
gen_math_ops
.
is_nan
(
input
,
name
)
;
/// <summary>
/// Returns element-wise smallest integer not less than x.
/// </summary>
/// <param name="x"></param>
/// <param name="name"></param>
/// <returns></returns>
public
Tensor
ceil
(
Tensor
x
,
string
name
=
null
)
=>
gen_math_ops
.
ceil
(
x
,
name
)
;
/// <summary>
/// Computes sin of x element-wise.
/// </summary>
/// <param name="x"></param>
/// <param name="name"></param>
/// <returns></returns>
public
Tensor
sin
(
Tensor
x
,
string
name
=
null
)
=>
gen_math_ops
.
sin
(
x
,
name
)
;
/// <summary>
/// Computes hyperbolic sine of x element-wise.
/// </summary>
/// <param name="x"></param>
/// <param name="name"></param>
/// <returns></returns>
public
Tensor
sinh
(
Tensor
x
,
string
name
=
null
)
=>
gen_math_ops
.
sinh
(
x
,
name
)
;
/// <summary>
/// Computes cos of x element-wise.
/// </summary>
/// <param name="x"></param>
/// <param name="name"></param>
/// <returns></returns>
public
Tensor
cos
(
Tensor
x
,
string
name
=
null
)
=>
gen_math_ops
.
cos
(
x
,
name
)
;
/// <summary>
/// Computes hyperbolic cosine of x element-wise.
/// </summary>
/// <param name="x"></param>
/// <param name="name"></param>
/// <returns></returns>
public
Tensor
cosh
(
Tensor
x
,
string
name
=
null
)
=>
gen_math_ops
.
cosh
(
x
,
name
)
;
public
Tensor
tan
(
Tensor
x
,
string
name
=
null
)
=>
gen_math_ops
.
tan
(
x
,
name
)
;
public
Tensor
tanh
(
Tensor
x
,
string
name
=
null
)
=>
gen_math_ops
.
tanh
(
x
,
name
)
;
/// <summary>
/// Returns element-wise largest integer not greater than x.
/// </summary>
/// <param name="x"></param>
/// <param name="name"></param>
/// <returns></returns>
public
Tensor
floor
(
Tensor
x
,
string
name
=
null
)
=>
gen_math_ops
.
floor
(
x
,
name
)
;
/// <summary>
/// Returns the truth value of (x > y) element-wise.
/// </summary>
/// <typeparam name="Tx"></typeparam>
/// <typeparam name="Ty"></typeparam>
/// <param name="x"></param>
/// <param name="y"></param>
/// <param name="name"></param>
/// <returns></returns>
public
Tensor
greater
<
Tx
,
Ty
>
(
Tx
x
,
Ty
y
,
string
name
=
null
)
=>
gen_math_ops
.
greater
(
x
,
y
,
name
)
;
/// <summary>
/// Returns the truth value of (x >= y) element-wise.
/// </summary>
/// <typeparam name="Tx"></typeparam>
/// <typeparam name="Ty"></typeparam>
/// <param name="x"></param>
/// <param name="y"></param>
/// <param name="name"></param>
/// <returns></returns>
public
Tensor
greater_equal
<
Tx
,
Ty
>
(
Tx
x
,
Ty
y
,
string
name
=
null
)
=>
gen_math_ops
.
greater_equal
(
x
,
y
,
name
)
;
/// <summary>
/// Returns the truth value of (x < y) element-wise.
/// </summary>
/// <typeparam name="Tx"></typeparam>
/// <typeparam name="Ty"></typeparam>
/// <param name="x"></param>
/// <param name="y"></param>
/// <param name="name"></param>
/// <returns></returns>
public
Tensor
less
<
Tx
,
Ty
>
(
Tx
x
,
Ty
y
,
string
name
=
null
)
=>
gen_math_ops
.
less
(
x
,
y
,
name
)
;
/// <summary>
/// Computes the log of the absolute value of `Gamma(x)` element-wise.
/// </summary>
/// <param name="x">A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`.</param>
/// <param name="name">A name for the operation (optional).</param>
/// <returns>A `Tensor`. Has the same type as `x`.</returns>
public
Tensor
lgamma
(
Tensor
x
,
string
name
=
null
)
=>
gen_math_ops
.
lgamma
(
x
,
name
:
name
)
;
/// <summary>
/// Returns the truth value of (x <= y) element-wise.
/// </summary>
/// <typeparam name="Tx"></typeparam>
/// <typeparam name="Ty"></typeparam>
/// <param name="x"></param>
/// <param name="y"></param>
/// <param name="name"></param>
/// <returns></returns>
public
Tensor
less_equal
<
Tx
,
Ty
>
(
Tx
x
,
Ty
y
,
string
name
=
null
)
=>
gen_math_ops
.
less_equal
(
x
,
y
,
name
)
;
/// <summary>
/// Computes natural logarithm of (1 + x) element-wise.
/// </summary>
/// <param name="x"></param>
/// <param name="name"></param>
/// <returns></returns>
public
Tensor
log1p
(
Tensor
x
,
string
name
=
null
)
=>
gen_math_ops
.
log1p
(
x
,
name
)
;
public
Tensor
logical_and
(
Tensor
x
,
Tensor
y
,
string
name
=
null
)
=>
gen_math_ops
.
logical_and
(
x
,
y
,
name
)
;
public
Tensor
logical_not
(
Tensor
x
,
string
name
=
null
)
=>
gen_math_ops
.
logical_not
(
x
,
name
)
;
public
Tensor
logical_or
(
Tensor
x
,
Tensor
y
,
string
name
=
null
)
=>
gen_math_ops
.
logical_or
(
x
,
y
,
name
)
;
public
Tensor
logical_xor
(
Tensor
x
,
Tensor
y
,
string
name
=
"LogicalXor"
)
=>
gen_math_ops
.
logical_xor
(
x
,
y
,
name
)
;
/// <summary>
/// Clips tensor values to a specified min and max.
/// </summary>
/// <param name="t"></param>
/// <param name="clip_value_min"></param>
/// <param name="clip_value_max"></param>
/// <param name="name"></param>
/// <returns></returns>
public
Tensor
_clip_by_value
(
Tensor
t
,
Tensor
clip_value_min
,
Tensor
clip_value_max
,
string
name
=
null
)
=>
gen_math_ops
.
_clip_by_value
(
t
,
clip_value_min
,
clip_value_max
)
;
/// <summary>
/// Clips tensor values to a specified min and max.
/// </summary>
/// <param name="t">
/// A <c>Tensor</c>.
/// </param>
/// <param name="clip_value_min">
/// A 0-D (scalar) <c>Tensor</c>, or a <c>Tensor</c> with the same shape
/// as <c>t</c>. The minimum value to clip by.
/// </param>
/// <param name="clip_value_max">
/// A 0-D (scalar) <c>Tensor</c>, or a <c>Tensor</c> with the same shape
/// as <c>t</c>. The maximum value to clip by.
/// </param>
/// <param name="name">
/// If specified, the created operation in the graph will be this one, otherwise it will be named 'ClipByValue'.
/// </param>
/// <returns>
/// A clipped <c>Tensor</c> with the same shape as input 't'.
/// The Operation can be fetched from the resulting Tensor, by fetching the Operation property from the result.
/// </returns>
/// <remarks>
/// Given a tensor <c>t</c>, this operation returns a tensor of the same type and
/// shape as <c>t</c> with its values clipped to <c>clip_value_min</c> and <c>clip_value_max</c>.
/// Any values less than <c>clip_value_min</c> are set to <c>clip_value_min</c>. Any values
/// greater than <c>clip_value_max</c> are set to <c>clip_value_max</c>.
/// </remarks>
public
Tensor
clip_by_value
<
T1
,
T2
>
(
Tensor
t
,
T1
clip_value_min
,
T2
clip_value_max
,
string
name
=
"ClipByValue"
)
=>
clip_ops
.
clip_by_value
(
t
,
clip_value_min
,
clip_value_max
,
name
)
;
public
Tensor
sub
<
Tx
,
Ty
>
(
Tx
a
,
Ty
b
,
string
name
=
null
)
=>
gen_math_ops
.
sub
(
a
,
b
,
name
:
name
)
;
public
Tensor
divide
(
Tensor
a
,
Tensor
b
)
=>
a
/
b
;
public
Tensor
sqrt
(
Tensor
a
,
string
name
=
null
)
=>
gen_math_ops
.
sqrt
(
a
,
name
)
;
public
Tensor
sign
(
Tensor
a
,
string
name
=
null
)
=>
gen_math_ops
.
sign
(
a
,
name
)
;
public
Tensor
subtract
<
T
>
(
Tensor
x
,
T
[
]
y
,
string
name
=
null
)
where
T
:
struct
=>
gen_math_ops
.
sub
(
x
,
ops
.
convert_to_tensor
(
y
,
dtype
:
x
.
dtype
.
as_base_dtype
(
)
,
name
:
"y"
)
,
name
)
;
/// <summary>
/// return x - y
/// </summary>
/// <param name="x"></param>
/// <param name="y"></param>
/// <param name="name"></param>
/// <returns></returns>
public
Tensor
subtract
(
Tensor
x
,
Tensor
y
,
string
name
=
null
)
=>
gen_math_ops
.
sub
(
x
,
y
,
name
)
;
public
Tensor
log
(
Tensor
x
,
string
name
=
null
)
=>
gen_math_ops
.
log
(
x
,
name
)
;
public
Tensor
equal
(
Tensor
x
,
Tensor
y
,
string
name
=
null
)
=>
gen_math_ops
.
equal
(
x
,
y
,
name
)
;
/// <summary>
/// Computes arctangent of `y/x` element-wise, respecting signs of the arguments.
/// </summary>
/// <param name="y"></param>
/// <param name="x"></param>
/// <param name="name"></param>
/// <returns></returns>
public
Tensor
atan2
(
Tensor
y
,
Tensor
x
,
string
name
=
null
)
=>
gen_math_ops
.
atan2
(
y
,
x
,
name
)
;
/// <summary>
/// Computes the maximum of elements across dimensions of a tensor.
/// </summary>
/// <typeparam name="Tx"></typeparam>
/// <typeparam name="Ty"></typeparam>
/// <param name="input"></param>
/// <param name="axis"></param>
/// <param name="keep_dims"></param>
/// <param name="name"></param>
/// <returns></returns>
public
Tensor
max
<
Tx
,
Ty
>
(
Tx
input
,
Ty
axis
,
bool
keep_dims
=
false
,
string
name
=
null
)
=>
gen_math_ops
.
_max
(
input
,
axis
,
keep_dims
:
keep_dims
,
name
:
name
)
;
/// <summary>
/// Computes the minimum of elements across dimensions of a tensor.
/// </summary>
/// <typeparam name="Tx"></typeparam>
/// <typeparam name="Ty"></typeparam>
/// <param name="input"></param>
/// <param name="axis"></param>
/// <param name="keep_dims"></param>
/// <param name="name"></param>
/// <returns></returns>
public
Tensor
min
<
Tx
,
Ty
>
(
Tx
input
,
Ty
axis
,
bool
keep_dims
=
false
,
string
name
=
null
)
=>
gen_math_ops
.
_min
(
input
,
axis
,
keep_dims
:
keep_dims
,
name
:
name
)
;
/// <summary>
/// Returns the max of x and y (i.e. x > y ? x : y) element-wise.
/// </summary>
/// <typeparam name="T1"></typeparam>
/// <typeparam name="T2"></typeparam>
/// <param name="x"></param>
/// <param name="y"></param>
/// <param name="name"></param>
/// <returns></returns>
public
Tensor
maximum
<
T1
,
T2
>
(
T1
x
,
T2
y
,
string
name
=
null
)
=>
gen_math_ops
.
maximum
(
x
,
y
,
name
:
name
)
;
/// <summary>
/// Returns the min of x and y (i.e. x < y ? x : y) element-wise.
/// </summary>
/// <typeparam name="T1"></typeparam>
/// <typeparam name="T2"></typeparam>
/// <param name="x"></param>
/// <param name="y"></param>
/// <param name="name"></param>
/// <returns></returns>
public
Tensor
minimum
<
T1
,
T2
>
(
T1
x
,
T2
y
,
string
name
=
null
)
=>
gen_math_ops
.
minimum
(
x
,
y
,
name
:
name
)
;
public
Tensor
multiply
(
Tensor
x
,
Tensor
y
,
string
name
=
null
)
=>
gen_math_ops
.
mul
(
x
,
y
,
name
:
name
)
;
/// <summary>
/// return x * y
/// </summary>
/// <typeparam name="Tx"></typeparam>
/// <typeparam name="Ty"></typeparam>
/// <param name="x"></param>
/// <param name="y"></param>
/// <param name="name"></param>
/// <returns></returns>
public
Tensor
multiply
<
Tx
,
Ty
>
(
Tx
x
,
Ty
y
,
string
name
=
null
)
=>
gen_math_ops
.
mul
(
x
,
y
,
name
:
name
)
;
public
Tensor
negative
(
Tensor
x
,
string
name
=
null
)
=>
gen_math_ops
.
neg
(
x
,
name
)
;
/// <summary>
/// Returns the truth value of (x != y) element-wise.
/// </summary>
/// <param name="x"></param>
/// <param name="y"></param>
/// <param name="name"></param>
/// <returns>A `Tensor` of type bool with the same size as that of x or y.</returns>
public
Tensor
not_equal
<
Tx
,
Ty
>
(
Tx
x
,
Ty
y
,
string
name
=
null
)
=>
math_ops
.
not_equal
(
x
,
y
,
name
:
name
)
;
/// <summary>
/// Divides x / y elementwise (using Python 2 division operator semantics).
/// </summary>
/// <param name="x"></param>
/// <param name="y"></param>
/// <param name="name"></param>
/// <returns></returns>
public
Tensor
div
(
Tensor
x
,
Tensor
y
,
string
name
=
null
)
=>
math_ops
.
div
(
x
,
y
,
name
:
name
)
;
public
Tensor
divide
<
T
>
(
Tensor
x
,
T
[
]
y
,
string
name
=
null
)
where
T
:
struct
=>
x
/
ops
.
convert_to_tensor
(
y
,
dtype
:
x
.
dtype
.
as_base_dtype
(
)
,
name
:
"y"
)
;
public
Tensor
pow
<
T1
,
T2
>
(
T1
x
,
T2
y
,
string
name
=
"pow"
)
=>
math_ops
.
pow
(
x
,
y
,
name
:
name
)
;
/// <summary>
/// Divides `x / y` elementwise, rounding toward the most negative integer.
/// </summary>
/// <param name="x"></param>
/// <param name="y"></param>
/// <param name="name"></param>
/// <returns>`x / y` rounded down.</returns>
public
Tensor
floordiv
(
Tensor
x
,
Tensor
y
,
string
name
=
null
)
=>
math_ops
.
floordiv
(
x
,
y
,
name
:
name
)
;
/// <summary>
/// Divides x / y elementwise (using Python 3 division operator semantics).
/// </summary>
/// <param name="x"></param>
/// <param name="y"></param>
/// <param name="name"></param>
/// <returns>`x / y` evaluated in floating point.</returns>
public
static
Tensor
truediv
(
Tensor
x
,
Tensor
y
,
string
name
=
null
)
=>
math_ops
.
truediv
(
x
,
y
,
name
:
name
)
;
public
Tensor
range
(
object
start
,
object
limit
=
null
,
object
delta
=
null
,
TF_DataType
dtype
=
TF_DataType
.
DtInvalid
,
string
name
=
"range"
)
=>
math_ops
.
range
(
start
,
limit
:
limit
,
delta
:
delta
,
dtype
:
dtype
,
name
:
name
)
;
/// <summary>
/// Computes the "logical or" of elements across dimensions of a tensor.
/// </summary>
/// <param name="input_tensor">The boolean tensor to reduce.</param>
/// <param name="axis">The dimensions to reduce.</param>
/// <param name="keepdims">If true, retains reduced dimensions with length 1.</param>
/// <param name="name"></param>
/// <returns>The reduced tensor.</returns>
public
Tensor
reduce_any
(
Tensor
input_tensor
,
int
[
]
axis
=
null
,
bool
keepdims
=
false
,
string
name
=
null
)
=>
math_ops
.
reduce_any
(
input_tensor
,
axis
:
axis
,
keepdims
:
keepdims
,
name
:
name
)
;
public
Tensor
reduce_any
(
Tensor
input_tensor
,
int
axis
=
0
,
bool
keepdims
=
false
,
string
name
=
null
)
=>
math_ops
.
reduce_any
(
input_tensor
,
axis
:
new
[
]
{
axis
}
,
keepdims
:
keepdims
,
name
:
name
)
;
/// <summary>
/// Computes the "logical and" of elements across dimensions of a tensor.
/// </summary>
/// <param name="input_tensor"></param>
/// <param name="axis"></param>
/// <param name="keepdims"></param>
/// <param name="name"></param>
/// <returns>The reduced tensor.</returns>
public
Tensor
reduce_all
(
Tensor
input_tensor
,
int
[
]
axis
=
null
,
bool
keepdims
=
false
,
string
name
=
null
)
=>
math_ops
.
reduce_all
(
input_tensor
,
axis
:
axis
,
keepdims
:
keepdims
,
name
:
name
)
;
/// <summary>
/// Computes the product of elements across dimensions of a tensor.
/// </summary>
/// <param name="input_tensor"></param>
/// <param name="axis"></param>
/// <param name="keepdims"></param>
/// <param name="name"></param>
/// <returns></returns>
public
Tensor
reduce_prod
(
Tensor
input_tensor
,
int
[
]
axis
=
null
,
bool
keepdims
=
false
,
string
name
=
null
)
=>
math_ops
.
reduce_prod
(
input_tensor
,
axis
:
axis
,
keepdims
:
keepdims
,
name
:
name
)
;
/// <summary>
/// Computes the sum of elements across dimensions of a tensor.
/// </summary>
/// <param name="input_tensors"></param>
/// <param name="axis"></param>
/// <param name="keepdims"></param>
/// <param name="name"></param>
/// <returns></returns>
public
Tensor
reduce_sum
(
Tensor
[
]
input_tensors
,
int
?
axis
=
null
,
bool
keepdims
=
false
,
string
name
=
null
)
=>
math_ops
.
reduce_sum
(
input_tensors
,
axis
:
axis
,
keepdims
:
keepdims
,
name
:
name
)
;
/// <summary>
/// Computes the sum of elements across dimensions of a tensor.
/// </summary>
/// <param name="input"></param>
/// <param name="axis"></param>
/// <returns></returns>
public
Tensor
reduce_sum
(
Tensor
input
,
int
?
axis
=
null
,
int
?
reduction_indices
=
null
,
bool
keepdims
=
false
,
string
name
=
null
)
{
if
(
!
axis
.
HasValue
&&
reduction_indices
.
HasValue
&&
!
keepdims
)
return
math_ops
.
reduce_sum
(
input
,
reduction_indices
.
Value
)
;
else
if
(
axis
.
HasValue
&&
!
reduction_indices
.
HasValue
&&
!
keepdims
)
return
math_ops
.
reduce_sum
(
input
,
axis
.
Value
)
;
else
if
(
axis
.
HasValue
&&
!
reduction_indices
.
HasValue
&&
keepdims
)
return
math_ops
.
reduce_sum
(
input
,
keepdims
:
keepdims
,
axis
:
axis
.
Value
,
name
:
name
)
;
else
return
math_ops
.
reduce_sum
(
input
,
keepdims
:
keepdims
,
name
:
name
)
;
}
public
Tensor
reduce_sum
(
Tensor
input
,
TensorShape
axis
,
int
?
reduction_indices
=
null
,
bool
keepdims
=
false
,
string
name
=
null
)
=>
math_ops
.
reduce_sum
(
input
,
axis
,
keepdims
:
keepdims
,
name
:
name
)
;
/// <summary>
/// Computes the maximum of elements across dimensions of a tensor.
/// </summary>
/// <param name="input_tensor"></param>
/// <param name="axis"></param>
/// <param name="keepdims"></param>
/// <param name="name"></param>
/// <returns></returns>
public
Tensor
reduce_max
(
Tensor
input_tensor
,
int
[
]
axis
=
null
,
bool
keepdims
=
false
,
string
name
=
null
)
=>
math_ops
.
reduce_max
(
input_tensor
,
axis
,
keepdims
,
name
)
;
public
Tensor
reduce_max
(
Tensor
input_tensor
,
int
axis
,
bool
keepdims
=
false
,
string
name
=
null
)
=>
math_ops
.
reduce_max
(
input_tensor
,
axis
,
keepdims
,
name
)
;
public
Tensor
reduce_min
(
Tensor
input_tensor
,
int
[
]
axis
=
null
,
bool
keepdims
=
false
,
string
name
=
null
)
=>
math_ops
.
reduce_min
(
input_tensor
,
axis
,
keepdims
,
name
)
;
public
Tensor
sigmoid
<
T
>
(
T
x
,
string
name
=
null
)
=>
math_ops
.
sigmoid
(
x
,
name
:
name
)
;
public
Tensor
sum
(
Tensor
input
,
int
axis
,
bool
keep_dims
=
false
,
string
name
=
null
)
=>
gen_math_ops
.
_sum
(
input
,
axis
,
keep_dims
:
keep_dims
,
name
:
name
)
;
public
Tensor
reduce_mean
(
Tensor
input_tensor
,
int
[
]
axis
=
null
,
bool
keepdims
=
false
,
string
name
=
null
,
int
?
reduction_indices
=
null
)
=>
math_ops
.
reduce_mean
(
input_tensor
,
axis
:
axis
,
keepdims
:
keepdims
,
name
:
name
,
reduction_indices
:
reduction_indices
)
;
public
Tensor
reduce_mean
(
Tensor
[
]
input_tensors
,
int
?
axis
=
null
,
bool
keepdims
=
false
,
string
name
=
null
)
=>
math_ops
.
reduce_mean
(
input_tensors
,
axis
:
axis
,
keepdims
:
keepdims
,
name
:
name
)
;
public
Tensor
round
(
Tensor
x
,
string
name
=
null
)
=>
gen_math_ops
.
round
(
x
,
name
:
name
)
;
public
Tensor
cast
(
Tensor
x
,
TF_DataType
dtype
=
TF_DataType
.
DtInvalid
,
string
name
=
null
)
=>
math_ops
.
cast
(
x
,
dtype
,
name
)
;
public
Tensor
cumsum
(
Tensor
x
,
int
axis
=
0
,
bool
exclusive
=
false
,
bool
reverse
=
false
,
string
name
=
null
)
=>
math_ops
.
cumsum
(
x
,
axis
:
axis
,
exclusive
:
exclusive
,
reverse
:
reverse
,
name
:
name
)
;
public
Tensor
argmax
(
Tensor
input
,
int
axis
=
-
1
,
string
name
=
null
,
int
?
dimension
=
null
,
TF_DataType
output_type
=
TF_DataType
.
TF_INT64
)
=>
gen_math_ops
.
arg_max
(
input
,
axis
,
name
:
name
,
output_type
:
output_type
)
;
public
Tensor
square
(
Tensor
x
,
string
name
=
null
)
=>
gen_math_ops
.
square
(
x
,
name
:
name
)
;
public
Tensor
squared_difference
(
Tensor
x
,
Tensor
y
,
string
name
=
null
)
=>
gen_math_ops
.
squared_difference
(
x
:
x
,
y
:
y
,
name
:
name
)
;
}
}
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