1 | // This file is MACHINE GENERATED! Do not edit. |
2 | |
3 | |
4 | #include "tensorflow/cc/ops/const_op.h" |
5 | #include "tensorflow/cc/ops/random_ops.h" |
6 | |
7 | namespace tensorflow { |
8 | namespace ops { |
9 | |
10 | Multinomial::Multinomial(const ::tensorflow::Scope& scope, ::tensorflow::Input |
11 | logits, ::tensorflow::Input num_samples, const |
12 | Multinomial::Attrs& attrs) { |
13 | if (!scope.ok()) return; |
14 | auto _logits = ::tensorflow::ops::AsNodeOut(scope, logits); |
15 | if (!scope.ok()) return; |
16 | auto _num_samples = ::tensorflow::ops::AsNodeOut(scope, num_samples); |
17 | if (!scope.ok()) return; |
18 | ::tensorflow::Node* ret; |
19 | const auto unique_name = scope.GetUniqueNameForOp("Multinomial" ); |
20 | auto builder = ::tensorflow::NodeBuilder(unique_name, "Multinomial" ) |
21 | .Input(_logits) |
22 | .Input(_num_samples) |
23 | .Attr("seed" , attrs.seed_) |
24 | .Attr("seed2" , attrs.seed2_) |
25 | .Attr("output_dtype" , attrs.output_dtype_) |
26 | ; |
27 | scope.UpdateBuilder(&builder); |
28 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
29 | if (!scope.ok()) return; |
30 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
31 | this->operation = Operation(ret); |
32 | this->output = Output(ret, 0); |
33 | } |
34 | |
35 | Multinomial::Multinomial(const ::tensorflow::Scope& scope, ::tensorflow::Input |
36 | logits, ::tensorflow::Input num_samples) |
37 | : Multinomial(scope, logits, num_samples, Multinomial::Attrs()) {} |
38 | |
39 | ParameterizedTruncatedNormal::ParameterizedTruncatedNormal(const |
40 | ::tensorflow::Scope& |
41 | scope, |
42 | ::tensorflow::Input |
43 | shape, |
44 | ::tensorflow::Input |
45 | means, |
46 | ::tensorflow::Input |
47 | stdevs, |
48 | ::tensorflow::Input |
49 | minvals, |
50 | ::tensorflow::Input |
51 | maxvals, const |
52 | ParameterizedTruncatedNormal::Attrs& |
53 | attrs) { |
54 | if (!scope.ok()) return; |
55 | auto _shape = ::tensorflow::ops::AsNodeOut(scope, shape); |
56 | if (!scope.ok()) return; |
57 | auto _means = ::tensorflow::ops::AsNodeOut(scope, means); |
58 | if (!scope.ok()) return; |
59 | auto _stdevs = ::tensorflow::ops::AsNodeOut(scope, stdevs); |
60 | if (!scope.ok()) return; |
61 | auto _minvals = ::tensorflow::ops::AsNodeOut(scope, minvals); |
62 | if (!scope.ok()) return; |
63 | auto _maxvals = ::tensorflow::ops::AsNodeOut(scope, maxvals); |
64 | if (!scope.ok()) return; |
65 | ::tensorflow::Node* ret; |
66 | const auto unique_name = scope.GetUniqueNameForOp("ParameterizedTruncatedNormal" ); |
67 | auto builder = ::tensorflow::NodeBuilder(unique_name, "ParameterizedTruncatedNormal" ) |
68 | .Input(_shape) |
69 | .Input(_means) |
70 | .Input(_stdevs) |
71 | .Input(_minvals) |
72 | .Input(_maxvals) |
73 | .Attr("seed" , attrs.seed_) |
74 | .Attr("seed2" , attrs.seed2_) |
75 | ; |
76 | scope.UpdateBuilder(&builder); |
77 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
78 | if (!scope.ok()) return; |
79 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
80 | this->operation = Operation(ret); |
81 | this->output = Output(ret, 0); |
82 | } |
83 | |
84 | ParameterizedTruncatedNormal::ParameterizedTruncatedNormal(const |
85 | ::tensorflow::Scope& |
86 | scope, |
87 | ::tensorflow::Input |
88 | shape, |
89 | ::tensorflow::Input |
90 | means, |
91 | ::tensorflow::Input |
92 | stdevs, |
93 | ::tensorflow::Input |
94 | minvals, |
95 | ::tensorflow::Input |
96 | maxvals) |
97 | : ParameterizedTruncatedNormal(scope, shape, means, stdevs, minvals, maxvals, ParameterizedTruncatedNormal::Attrs()) {} |
98 | |
99 | RandomGamma::RandomGamma(const ::tensorflow::Scope& scope, ::tensorflow::Input |
100 | shape, ::tensorflow::Input alpha, const |
101 | RandomGamma::Attrs& attrs) { |
102 | if (!scope.ok()) return; |
103 | auto _shape = ::tensorflow::ops::AsNodeOut(scope, shape); |
104 | if (!scope.ok()) return; |
105 | auto _alpha = ::tensorflow::ops::AsNodeOut(scope, alpha); |
106 | if (!scope.ok()) return; |
107 | ::tensorflow::Node* ret; |
108 | const auto unique_name = scope.GetUniqueNameForOp("RandomGamma" ); |
109 | auto builder = ::tensorflow::NodeBuilder(unique_name, "RandomGamma" ) |
110 | .Input(_shape) |
111 | .Input(_alpha) |
112 | .Attr("seed" , attrs.seed_) |
113 | .Attr("seed2" , attrs.seed2_) |
114 | ; |
115 | scope.UpdateBuilder(&builder); |
116 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
117 | if (!scope.ok()) return; |
118 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
119 | this->operation = Operation(ret); |
120 | this->output = Output(ret, 0); |
121 | } |
122 | |
123 | RandomGamma::RandomGamma(const ::tensorflow::Scope& scope, ::tensorflow::Input |
124 | shape, ::tensorflow::Input alpha) |
125 | : RandomGamma(scope, shape, alpha, RandomGamma::Attrs()) {} |
126 | |
127 | RandomPoissonV2::RandomPoissonV2(const ::tensorflow::Scope& scope, |
128 | ::tensorflow::Input shape, ::tensorflow::Input |
129 | rate, const RandomPoissonV2::Attrs& attrs) { |
130 | if (!scope.ok()) return; |
131 | auto _shape = ::tensorflow::ops::AsNodeOut(scope, shape); |
132 | if (!scope.ok()) return; |
133 | auto _rate = ::tensorflow::ops::AsNodeOut(scope, rate); |
134 | if (!scope.ok()) return; |
135 | ::tensorflow::Node* ret; |
136 | const auto unique_name = scope.GetUniqueNameForOp("RandomPoissonV2" ); |
137 | auto builder = ::tensorflow::NodeBuilder(unique_name, "RandomPoissonV2" ) |
138 | .Input(_shape) |
139 | .Input(_rate) |
140 | .Attr("seed" , attrs.seed_) |
141 | .Attr("seed2" , attrs.seed2_) |
142 | .Attr("dtype" , attrs.dtype_) |
143 | ; |
144 | scope.UpdateBuilder(&builder); |
145 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
146 | if (!scope.ok()) return; |
147 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
148 | this->operation = Operation(ret); |
149 | this->output = Output(ret, 0); |
150 | } |
151 | |
152 | RandomPoissonV2::RandomPoissonV2(const ::tensorflow::Scope& scope, |
153 | ::tensorflow::Input shape, ::tensorflow::Input |
154 | rate) |
155 | : RandomPoissonV2(scope, shape, rate, RandomPoissonV2::Attrs()) {} |
156 | |
157 | RandomShuffle::RandomShuffle(const ::tensorflow::Scope& scope, |
158 | ::tensorflow::Input value, const |
159 | RandomShuffle::Attrs& attrs) { |
160 | if (!scope.ok()) return; |
161 | auto _value = ::tensorflow::ops::AsNodeOut(scope, value); |
162 | if (!scope.ok()) return; |
163 | ::tensorflow::Node* ret; |
164 | const auto unique_name = scope.GetUniqueNameForOp("RandomShuffle" ); |
165 | auto builder = ::tensorflow::NodeBuilder(unique_name, "RandomShuffle" ) |
166 | .Input(_value) |
167 | .Attr("seed" , attrs.seed_) |
168 | .Attr("seed2" , attrs.seed2_) |
169 | ; |
170 | scope.UpdateBuilder(&builder); |
171 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
172 | if (!scope.ok()) return; |
173 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
174 | this->operation = Operation(ret); |
175 | this->output = Output(ret, 0); |
176 | } |
177 | |
178 | RandomShuffle::RandomShuffle(const ::tensorflow::Scope& scope, |
179 | ::tensorflow::Input value) |
180 | : RandomShuffle(scope, value, RandomShuffle::Attrs()) {} |
181 | |
182 | RandomNormal::RandomNormal(const ::tensorflow::Scope& scope, |
183 | ::tensorflow::Input shape, DataType dtype, const |
184 | RandomNormal::Attrs& attrs) { |
185 | if (!scope.ok()) return; |
186 | auto _shape = ::tensorflow::ops::AsNodeOut(scope, shape); |
187 | if (!scope.ok()) return; |
188 | ::tensorflow::Node* ret; |
189 | const auto unique_name = scope.GetUniqueNameForOp("RandomNormal" ); |
190 | auto builder = ::tensorflow::NodeBuilder(unique_name, "RandomStandardNormal" ) |
191 | .Input(_shape) |
192 | .Attr("seed" , attrs.seed_) |
193 | .Attr("seed2" , attrs.seed2_) |
194 | .Attr("dtype" , dtype) |
195 | ; |
196 | scope.UpdateBuilder(&builder); |
197 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
198 | if (!scope.ok()) return; |
199 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
200 | this->operation = Operation(ret); |
201 | this->output = Output(ret, 0); |
202 | } |
203 | |
204 | RandomNormal::RandomNormal(const ::tensorflow::Scope& scope, |
205 | ::tensorflow::Input shape, DataType dtype) |
206 | : RandomNormal(scope, shape, dtype, RandomNormal::Attrs()) {} |
207 | |
208 | RandomUniform::RandomUniform(const ::tensorflow::Scope& scope, |
209 | ::tensorflow::Input shape, DataType dtype, const |
210 | RandomUniform::Attrs& attrs) { |
211 | if (!scope.ok()) return; |
212 | auto _shape = ::tensorflow::ops::AsNodeOut(scope, shape); |
213 | if (!scope.ok()) return; |
214 | ::tensorflow::Node* ret; |
215 | const auto unique_name = scope.GetUniqueNameForOp("RandomUniform" ); |
216 | auto builder = ::tensorflow::NodeBuilder(unique_name, "RandomUniform" ) |
217 | .Input(_shape) |
218 | .Attr("seed" , attrs.seed_) |
219 | .Attr("seed2" , attrs.seed2_) |
220 | .Attr("dtype" , dtype) |
221 | ; |
222 | scope.UpdateBuilder(&builder); |
223 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
224 | if (!scope.ok()) return; |
225 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
226 | this->operation = Operation(ret); |
227 | this->output = Output(ret, 0); |
228 | } |
229 | |
230 | RandomUniform::RandomUniform(const ::tensorflow::Scope& scope, |
231 | ::tensorflow::Input shape, DataType dtype) |
232 | : RandomUniform(scope, shape, dtype, RandomUniform::Attrs()) {} |
233 | |
234 | RandomUniformInt::RandomUniformInt(const ::tensorflow::Scope& scope, |
235 | ::tensorflow::Input shape, |
236 | ::tensorflow::Input minval, |
237 | ::tensorflow::Input maxval, const |
238 | RandomUniformInt::Attrs& attrs) { |
239 | if (!scope.ok()) return; |
240 | auto _shape = ::tensorflow::ops::AsNodeOut(scope, shape); |
241 | if (!scope.ok()) return; |
242 | auto _minval = ::tensorflow::ops::AsNodeOut(scope, minval); |
243 | if (!scope.ok()) return; |
244 | auto _maxval = ::tensorflow::ops::AsNodeOut(scope, maxval); |
245 | if (!scope.ok()) return; |
246 | ::tensorflow::Node* ret; |
247 | const auto unique_name = scope.GetUniqueNameForOp("RandomUniformInt" ); |
248 | auto builder = ::tensorflow::NodeBuilder(unique_name, "RandomUniformInt" ) |
249 | .Input(_shape) |
250 | .Input(_minval) |
251 | .Input(_maxval) |
252 | .Attr("seed" , attrs.seed_) |
253 | .Attr("seed2" , attrs.seed2_) |
254 | ; |
255 | scope.UpdateBuilder(&builder); |
256 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
257 | if (!scope.ok()) return; |
258 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
259 | this->operation = Operation(ret); |
260 | this->output = Output(ret, 0); |
261 | } |
262 | |
263 | RandomUniformInt::RandomUniformInt(const ::tensorflow::Scope& scope, |
264 | ::tensorflow::Input shape, |
265 | ::tensorflow::Input minval, |
266 | ::tensorflow::Input maxval) |
267 | : RandomUniformInt(scope, shape, minval, maxval, RandomUniformInt::Attrs()) {} |
268 | |
269 | TruncatedNormal::TruncatedNormal(const ::tensorflow::Scope& scope, |
270 | ::tensorflow::Input shape, DataType dtype, |
271 | const TruncatedNormal::Attrs& attrs) { |
272 | if (!scope.ok()) return; |
273 | auto _shape = ::tensorflow::ops::AsNodeOut(scope, shape); |
274 | if (!scope.ok()) return; |
275 | ::tensorflow::Node* ret; |
276 | const auto unique_name = scope.GetUniqueNameForOp("TruncatedNormal" ); |
277 | auto builder = ::tensorflow::NodeBuilder(unique_name, "TruncatedNormal" ) |
278 | .Input(_shape) |
279 | .Attr("seed" , attrs.seed_) |
280 | .Attr("seed2" , attrs.seed2_) |
281 | .Attr("dtype" , dtype) |
282 | ; |
283 | scope.UpdateBuilder(&builder); |
284 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
285 | if (!scope.ok()) return; |
286 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
287 | this->operation = Operation(ret); |
288 | this->output = Output(ret, 0); |
289 | } |
290 | |
291 | TruncatedNormal::TruncatedNormal(const ::tensorflow::Scope& scope, |
292 | ::tensorflow::Input shape, DataType dtype) |
293 | : TruncatedNormal(scope, shape, dtype, TruncatedNormal::Attrs()) {} |
294 | |
295 | /// @} |
296 | |
297 | } // namespace ops |
298 | } // namespace tensorflow |
299 | |