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TensorFlow.NET/src/TensorFlowNET.Core/APIs/tf.random.cs at master · 591094733/TensorFlow.NET · GitHub
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TensorFlow.NET
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src
/
TensorFlowNET.Core
/
APIs
/
tf.random.cs
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TensorFlow.NET
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TensorFlowNET.Core
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APIs
/
tf.random.cs
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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.
******************************************************************************/
namespace
Tensorflow
{
public
partial
class
tensorflow
{
public
Random
random
=>
new
Random
(
)
;
public
class
Random
{
/// <summary>
/// Outputs random values from a normal distribution.
/// </summary>
/// <param name="shape"></param>
/// <param name="mean"></param>
/// <param name="stddev"></param>
/// <param name="dtype"></param>
/// <param name="seed"></param>
/// <param name="name"></param>
/// <returns></returns>
public
Tensor
normal
(
Shape
shape
,
float
mean
=
0.0f
,
float
stddev
=
1.0f
,
TF_DataType
dtype
=
TF_DataType
.
TF_FLOAT
,
int
?
seed
=
null
,
string
name
=
null
)
=>
random_ops
.
random_normal
(
shape
,
mean
,
stddev
,
dtype
,
seed
,
name
)
;
/// <summary>
/// Outputs random values from a truncated normal distribution.
/// </summary>
/// <param name="shape"></param>
/// <param name="mean"></param>
/// <param name="stddev"></param>
/// <param name="dtype"></param>
/// <param name="seed"></param>
/// <param name="name"></param>
/// <returns></returns>
public
Tensor
truncated_normal
(
Shape
shape
,
float
mean
=
0.0f
,
float
stddev
=
1.0f
,
TF_DataType
dtype
=
TF_DataType
.
TF_FLOAT
,
int
?
seed
=
null
,
string
name
=
null
)
=>
random_ops
.
truncated_normal
(
shape
,
mean
,
stddev
,
dtype
,
seed
,
name
)
;
public
Tensor
categorical
(
Tensor
logits
,
int
num_samples
,
int
?
seed
=
null
,
string
name
=
null
,
TF_DataType
output_dtype
=
TF_DataType
.
DtInvalid
)
=>
random_ops
.
multinomial
(
logits
,
num_samples
,
seed
:
seed
,
name
:
name
,
output_dtype
:
output_dtype
)
;
public
Tensor
uniform
(
Shape
shape
,
float
minval
=
0
,
float
maxval
=
1
,
TF_DataType
dtype
=
TF_DataType
.
TF_FLOAT
,
int
?
seed
=
null
,
string
name
=
null
)
{
if
(
dtype
.
is_integer
(
)
)
return
random_ops
.
random_uniform_int
(
shape
,
(
int
)
minval
,
(
int
)
maxval
,
seed
,
name
)
;
else
return
random_ops
.
random_uniform
(
shape
,
minval
,
maxval
,
dtype
,
seed
,
name
)
;
}
}
public
Tensor
random_uniform
(
Shape
shape
,
float
minval
=
0
,
float
maxval
=
1
,
TF_DataType
dtype
=
TF_DataType
.
TF_FLOAT
,
int
?
seed
=
null
,
string
name
=
null
)
=>
random
.
uniform
(
shape
,
minval
:
minval
,
maxval
:
maxval
,
dtype
:
dtype
,
seed
:
seed
,
name
:
name
)
;
public
Tensor
truncated_normal
(
Shape
shape
,
float
mean
=
0.0f
,
float
stddev
=
1.0f
,
TF_DataType
dtype
=
TF_DataType
.
TF_FLOAT
,
int
?
seed
=
null
,
string
name
=
null
)
=>
random_ops
.
truncated_normal
(
shape
,
mean
,
stddev
,
dtype
,
seed
,
name
)
;
/// <summary>
/// Randomly shuffles a tensor along its first dimension.
/// </summary>
/// <param name="value"></param>
/// <param name="seed"></param>
/// <param name="name"></param>
/// <returns>
/// A tensor of same shape and type as value, shuffled along its
/// first dimension.
/// </returns>
public
Tensor
random_shuffle
(
Tensor
value
,
int
?
seed
=
null
,
string
name
=
null
)
=>
random_ops
.
random_shuffle
(
value
,
seed
:
seed
,
name
:
name
)
;
public
void
set_random_seed
(
int
seed
)
{
if
(
executing_eagerly
(
)
)
Context
.
set_global_seed
(
seed
)
;
else
ops
.
get_default_graph
(
)
.
seed
=
seed
;
}
public
Tensor
multinomial
(
Tensor
logits
,
int
num_samples
,
int
?
seed
=
null
,
string
name
=
null
,
TF_DataType
output_dtype
=
TF_DataType
.
DtInvalid
)
=>
random_ops
.
multinomial
(
logits
,
num_samples
,
seed
:
seed
,
name
:
name
,
output_dtype
:
output_dtype
)
;
}
}
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