FazBrowse GitHub Viewer
|
Trending
|
URL:
|
Home
Tools:
[Download Repo ZIP]
[View Raw Code]
[Original HTTPS Page]
TensorFlow.NET/src/TensorFlowNET.Core/APIs/tf.array.cs at master · BOYMMM/TensorFlow.NET · GitHub
BOYMMM
/
TensorFlow.NET
Public
forked from
SciSharp/TensorFlow.NET
Notifications
You must be signed in to change notification settings
Fork
0
Star
0
Code
Pull requests
0
Actions
Projects
Security and quality
0
Insights
Additional navigation options
Code
Pull requests
Actions
Projects
Security and quality
Insights
Expand file tree
Breadcrumbs
TensorFlow.NET
/
src
/
TensorFlowNET.Core
/
APIs
/
tf.array.cs
Copy path
More file actions
More file actions
Latest commit
History
History
History
299 lines (267 loc) · 13 KB
Breadcrumbs
TensorFlow.NET
/
src
/
TensorFlowNET.Core
/
APIs
/
tf.array.cs
Copy path
File metadata and controls
299 lines (267 loc) · 13 KB
Raw
Copy raw file
Download raw file
Open symbols panel
Edit and raw actions
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
/*****************************************************************************
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
NumSharp
;
using
System
;
using
System
.
Collections
.
Generic
;
using
System
.
Diagnostics
;
using
System
.
Linq
;
using
static
Tensorflow
.
Binding
;
namespace
Tensorflow
{
public
partial
class
tensorflow
{
/// <summary>
/// A convenient alias for None, useful for indexing arrays.
/// </summary>
public
Slice
newaxis
=
Slice
.
NewAxis
;
/// <summary>
/// BatchToSpace for N-D tensors of type T.
/// </summary>
/// <typeparam name="T"></typeparam>
/// <param name="input"></param>
/// <param name="block_shape"></param>
/// <param name="crops"></param>
/// <param name="name"></param>
/// <returns></returns>
public
Tensor
batch_to_space_nd
<
T
>
(
T
input
,
int
[
]
block_shape
,
int
[
,
]
crops
,
string
name
=
null
)
=>
gen_array_ops
.
batch_to_space_nd
(
input
,
block_shape
,
crops
,
name
:
name
)
;
/// <summary>
/// Apply boolean mask to tensor.
/// </summary>
/// <typeparam name="T1"></typeparam>
/// <typeparam name="T2"></typeparam>
/// <param name="tensor">N-D tensor.</param>
/// <param name="mask">K-D boolean tensor, K <= N and K must be known statically.</param>
/// <param name="name"></param>
/// <param name="axis">A 0-D int Tensor representing the axis in tensor to mask from. </param>
/// <returns>(N-K+1)-dimensional tensor populated by entries in tensor corresponding to True values in mask.</returns>
public
Tensor
boolean_mask
<
T1
,
T2
>
(
T1
tensor
,
T2
mask
,
string
name
=
"boolean_mask"
,
int
axis
=
0
)
=>
array_ops
.
boolean_mask
(
tensor
,
mask
,
name
:
name
,
axis
:
axis
)
;
/// <summary>
/// Broadcast an array for a compatible shape.
/// </summary>
/// <param name="input"></param>
/// <param name="shape"></param>
/// <param name="name"></param>
/// <returns></returns>
public
Tensor
broadcast_to
(
Tensor
input
,
TensorShape
shape
,
string
name
=
null
)
=>
gen_array_ops
.
broadcast_to
(
input
,
shape
,
name
:
name
)
;
public
Tensor
check_numerics
(
Tensor
tensor
,
string
message
,
string
name
=
null
)
=>
gen_array_ops
.
check_numerics
(
tensor
,
message
,
name
:
name
)
;
/// <summary>
/// Concatenates tensors along one dimension.
/// </summary>
/// <param name="values">A list of `Tensor` objects or a single `Tensor`.</param>
/// <param name="axis"></param>
/// <param name="name"></param>
/// <returns>A `Tensor` resulting from concatenation of the input tensors.</returns>
public
Tensor
concat
(
IList
<
Tensor
>
values
,
int
axis
,
string
name
=
"concat"
)
{
if
(
values
.
Count
==
1
)
{
return
tf_with
(
ops
.
name_scope
(
name
)
,
scope
=>
{
var
tensor
=
ops
.
convert_to_tensor
(
axis
,
name
:
"concat_dim"
,
dtype
:
dtypes
.
int32
)
;
Debug
.
Assert
(
tensor
.
TensorShape
.
ndim
==
0
)
;
return
identity
(
values
[
0
]
,
name
:
scope
)
;
}
)
;
}
return
gen_array_ops
.
concat_v2
(
values
.
ToArray
(
)
,
axis
,
name
:
name
)
;
}
/// <summary>
/// Inserts a dimension of 1 into a tensor's shape.
/// </summary>
/// <param name="input"></param>
/// <param name="axis"></param>
/// <param name="name"></param>
/// <param name="dim"></param>
/// <returns>
/// A `Tensor` with the same data as `input`, but its shape has an additional
/// dimension of size 1 added.
/// </returns>
public
Tensor
expand_dims
(
Tensor
input
,
int
axis
=
-
1
,
string
name
=
null
,
int
dim
=
-
1
)
=>
array_ops
.
expand_dims
(
input
,
axis
,
name
,
dim
)
;
/// <summary>
/// Creates a tensor filled with a scalar value.
/// </summary>
/// <param name="dims"></param>
/// <param name="value"></param>
/// <param name="name"></param>
/// <returns></returns>
public
Tensor
fill
<
T
>
(
Tensor
dims
,
T
value
,
string
name
=
null
)
=>
gen_array_ops
.
fill
(
dims
,
value
,
name
:
name
)
;
/// <summary>
/// Return a tensor with the same shape and contents as input.
/// </summary>
/// <param name="input"></param>
/// <param name="name"></param>
/// <returns></returns>
public
Tensor
identity
(
Tensor
input
,
string
name
=
null
)
=>
array_ops
.
identity
(
input
,
name
:
name
)
;
/// <summary>
/// Gather slices from params axis axis according to indices.
/// </summary>
/// <param name="params"></param>
/// <param name="indices"></param>
/// <param name="name"></param>
/// <param name="axis"></param>
/// <returns></returns>
public
Tensor
gather
(
Tensor
@params
,
Tensor
indices
,
string
name
=
null
,
int
axis
=
0
)
=>
array_ops
.
gather
(
@params
,
indices
,
name
:
name
,
axis
:
axis
)
;
/// <summary>
/// Return the elements, either from `x` or `y`, depending on the `condition`.
/// </summary>
/// <returns></returns>
public
Tensor
where
<
Tx
,
Ty
>
(
Tensor
condition
,
Tx
x
,
Ty
y
,
string
name
=
null
)
=>
array_ops
.
where
(
condition
,
x
,
y
,
name
)
;
/// <summary>
/// Transposes `a`. Permutes the dimensions according to `perm`.
/// </summary>
/// <param name="a"></param>
/// <param name="perm"></param>
/// <param name="name"></param>
/// <param name="conjugate"></param>
/// <returns></returns>
public
Tensor
transpose
<
T1
>
(
T1
a
,
int
[
]
perm
=
null
,
string
name
=
"transpose"
,
bool
conjugate
=
false
)
=>
array_ops
.
transpose
(
a
,
perm
,
name
,
conjugate
)
;
/// <summary>
/// Reverses specific dimensions of a tensor.
/// </summary>
/// <param name="tensor"></param>
/// <param name="axis"></param>
/// <param name="name"></param>
/// <returns></returns>
public
Tensor
reverse
(
Tensor
tensor
,
int
[
]
axis
,
string
name
=
null
)
=>
gen_array_ops
.
reverse
(
tensor
,
axis
,
name
:
name
)
;
public
Tensor
reverse
(
Tensor
tensor
,
Tensor
axis
,
string
name
=
null
)
=>
gen_array_ops
.
reverse
(
tensor
,
axis
,
name
:
name
)
;
/// <summary>
/// Returns the rank of a tensor.
/// </summary>
/// <param name="input"></param>
/// <param name="name"></param>
/// <returns>Returns a 0-D `int32` `Tensor` representing the rank of `input`.</returns>
public
Tensor
rank
(
Tensor
input
,
string
name
=
null
)
=>
array_ops
.
rank
(
input
,
name
:
name
)
;
/// <summary>
/// Extracts a slice from a tensor.
/// </summary>
/// <param name="input">A `Tensor`.</param>
/// <param name="begin">An `int32` or `int64` `Tensor`.</param>
/// <param name="size">An `int32` or `int64` `Tensor`.</param>
/// <param name="name">A name for the operation (optional).</param>
/// <returns>A `Tensor` the same type as `input`.</returns>
public
Tensor
slice
<
Tb
,
Ts
>
(
Tensor
input
,
Tb
[
]
begin
,
Ts
[
]
size
,
string
name
=
null
)
=>
array_ops
.
slice
(
input
,
begin
,
size
,
name
:
name
)
;
public
Tensor
squeeze
(
Tensor
input
,
int
[
]
axis
=
null
,
string
name
=
null
,
int
squeeze_dims
=
-
1
)
=>
gen_array_ops
.
squeeze
(
input
,
axis
,
name
)
;
/// <summary>
/// Stacks a list of rank-`R` tensors into one rank-`(R+1)` tensor.
/// </summary>
/// <param name="values"></param>
/// <param name="axis"></param>
/// <param name="name"></param>
/// <returns></returns>
public
Tensor
stack
(
object
values
,
int
axis
=
0
,
string
name
=
"stack"
)
=>
array_ops
.
stack
(
values
,
axis
,
name
:
name
)
;
/// <summary>
/// Creates a tensor with all elements set to 1.
/// </summary>
/// <param name="tensor"></param>
/// <param name="dtype"></param>
/// <param name="name">A name for the operation (optional).</param>
/// <param name="optimize">
/// if true, attempt to statically determine the shape of 'tensor' and
/// encode it as a constant.
/// </param>
/// <returns>A `Tensor` with all elements set to 1.</returns>
public
Tensor
ones_like
(
Tensor
tensor
,
TF_DataType
dtype
=
TF_DataType
.
DtInvalid
,
string
name
=
null
,
bool
optimize
=
true
)
=>
array_ops
.
ones_like
(
tensor
,
dtype
:
dtype
,
name
:
name
,
optimize
:
optimize
)
;
public
Tensor
one_hot
(
Tensor
indices
,
int
depth
,
Tensor
on_value
=
null
,
Tensor
off_value
=
null
,
TF_DataType
dtype
=
TF_DataType
.
DtInvalid
,
int
axis
=
-
1
,
string
name
=
null
)
=>
array_ops
.
one_hot
(
indices
,
depth
,
dtype
:
dtype
,
axis
:
axis
,
name
:
name
)
;
/// <summary>
/// Pads a tensor
/// </summary>
/// <param name="tensor"></param>
/// <param name="paddings"></param>
/// <param name="mode"></param>
/// <param name="name"></param>
/// <param name="constant_values"></param>
/// <returns></returns>
public
Tensor
pad
(
Tensor
tensor
,
Tensor
paddings
,
string
mode
=
"CONSTANT"
,
string
name
=
null
,
int
constant_values
=
0
)
=>
array_ops
.
pad
(
tensor
,
paddings
,
mode
:
mode
,
name
:
name
,
constant_values
:
constant_values
)
;
/// <summary>
/// A placeholder op that passes through `input` when its output is not fed.
/// </summary>
/// <typeparam name="T"></typeparam>
/// <param name="input">A `Tensor`. The default value to produce when output is not fed.</param>
/// <param name="shape">
/// A `tf.TensorShape` or list of `int`s. The (possibly partial) shape of
/// the tensor.
/// </param>
/// <param name="name">A name for the operation (optional).</param>
/// <returns>A `Tensor`. Has the same type as `input`.</returns>
public
Tensor
placeholder_with_default
<
T
>
(
T
input
,
int
[
]
shape
,
string
name
=
null
)
=>
gen_array_ops
.
placeholder_with_default
(
input
,
shape
,
name
:
name
)
;
/// <summary>
/// Returns the shape of a tensor.
/// </summary>
/// <param name="input"></param>
/// <param name="name"></param>
/// <param name="out_type"></param>
/// <returns></returns>
public
Tensor
shape
(
Tensor
input
,
string
name
=
null
,
TF_DataType
out_type
=
TF_DataType
.
TF_INT32
)
=>
array_ops
.
shape_internal
(
input
,
name
,
optimize
:
true
,
out_type
:
out_type
)
;
/// <summary>
/// Stacks a list of rank-`R` tensors into one rank-`(R+1)` tensor.
/// </summary>
/// <param name="values"></param>
/// <param name="axis"></param>
/// <param name="name"></param>
/// <returns>A stacked `Tensor` with the same type as `values`.</returns>
public
Tensor
stack
(
Tensor
[
]
values
,
int
axis
=
0
,
string
name
=
"stack"
)
=>
array_ops
.
stack
(
values
,
axis
:
axis
,
name
:
name
)
;
/// <summary>
/// Unpacks the given dimension of a rank-`R` tensor into rank-`(R-1)` tensors.
/// </summary>
/// <param name="value"></param>
/// <param name="num"></param>
/// <param name="axis"></param>
/// <param name="name"></param>
/// <returns></returns>
public
Tensor
[
]
unstack
(
Tensor
value
,
int
?
num
=
null
,
int
axis
=
0
,
string
name
=
"unstack"
)
=>
array_ops
.
unstack
(
value
,
num
:
num
,
axis
:
axis
,
name
:
name
)
;
/// <summary>
/// Creates a tensor with all elements set to zero.
/// </summary>
/// <param name="tensor"></param>
/// <param name="dtype"></param>
/// <param name="name"></param>
/// <param name="optimize"></param>
/// <returns>A `Tensor` with all elements set to zero.</returns>
public
Tensor
zeros_like
(
Tensor
tensor
,
TF_DataType
dtype
=
TF_DataType
.
DtInvalid
,
string
name
=
null
,
bool
optimize
=
true
)
=>
array_ops
.
zeros_like
(
tensor
,
dtype
:
dtype
,
name
:
name
,
optimize
:
optimize
)
;
/// <summary>
/// Stops gradient computation.
/// </summary>
/// <param name="x"></param>
/// <param name="name"></param>
/// <returns></returns>
public
Tensor
stop_gradient
(
Tensor
x
,
string
name
=
null
)
=>
gen_array_ops
.
stop_gradient
(
x
,
name
:
name
)
;
}
}
Back
|
FazBrowse Home
|
New Git URL