1 /*
2 * Copyright (C) 2017 The Android Open Source Project
3 *
4 * Licensed under the Apache License, Version 2.0 (the "License");
5 * you may not use this file except in compliance with the License.
6 * You may obtain a copy of the License at
7 *
8 * http://www.apache.org/licenses/LICENSE-2.0
9 *
10 * Unless required by applicable law or agreed to in writing, software
11 * distributed under the License is distributed on an "AS IS" BASIS,
12 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13 * See the License for the specific language governing permissions and
14 * limitations under the License.
15 */
16
17 #include "LSTM.h"
18
19 #include "NeuralNetworksWrapper.h"
20 #include "gmock/gmock-matchers.h"
21 #include "gtest/gtest.h"
22
23 namespace android {
24 namespace nn {
25 namespace wrapper {
26
27 using ::testing::Each;
28 using ::testing::FloatNear;
29 using ::testing::Matcher;
30
31 namespace {
32
ArrayFloatNear(const std::vector<float> & values,float max_abs_error=1.e-6)33 std::vector<Matcher<float>> ArrayFloatNear(const std::vector<float>& values,
34 float max_abs_error=1.e-6) {
35 std::vector<Matcher<float>> matchers;
36 matchers.reserve(values.size());
37 for (const float& v : values) {
38 matchers.emplace_back(FloatNear(v, max_abs_error));
39 }
40 return matchers;
41 }
42
43 } // anonymous namespace
44
45 #define FOR_ALL_INPUT_AND_WEIGHT_TENSORS(ACTION) \
46 ACTION(Input) \
47 ACTION(InputToInputWeights) \
48 ACTION(InputToCellWeights) \
49 ACTION(InputToForgetWeights) \
50 ACTION(InputToOutputWeights) \
51 ACTION(RecurrentToInputWeights) \
52 ACTION(RecurrentToCellWeights) \
53 ACTION(RecurrentToForgetWeights) \
54 ACTION(RecurrentToOutputWeights) \
55 ACTION(CellToInputWeights) \
56 ACTION(CellToForgetWeights) \
57 ACTION(CellToOutputWeights) \
58 ACTION(InputGateBias) \
59 ACTION(CellGateBias) \
60 ACTION(ForgetGateBias) \
61 ACTION(OutputGateBias) \
62 ACTION(ProjectionWeights) \
63 ACTION(ProjectionBias) \
64 ACTION(OutputStateIn) \
65 ACTION(CellStateIn)
66
67 // For all output and intermediate states
68 #define FOR_ALL_OUTPUT_TENSORS(ACTION) \
69 ACTION(ScratchBuffer) \
70 ACTION(OutputStateOut) \
71 ACTION(CellStateOut) \
72 ACTION(Output) \
73
74 class LSTMOpModel {
75 public:
LSTMOpModel(uint32_t n_batch,uint32_t n_input,uint32_t n_cell,uint32_t n_output,bool use_cifg,bool use_peephole,bool use_projection_weights,bool use_projection_bias,float cell_clip,float proj_clip,const std::vector<std::vector<uint32_t>> & input_shapes0)76 LSTMOpModel(uint32_t n_batch, uint32_t n_input,
77 uint32_t n_cell, uint32_t n_output, bool use_cifg,
78 bool use_peephole, bool use_projection_weights,
79 bool use_projection_bias, float cell_clip, float proj_clip,
80 const std::vector<std::vector<uint32_t>>& input_shapes0)
81 : n_input_(n_input),
82 n_output_(n_output),
83 use_cifg_(use_cifg), use_peephole_(use_peephole),
84 use_projection_weights_(use_projection_weights),
85 use_projection_bias_(use_projection_bias),
86 activation_(ActivationFn::kActivationTanh),
87 cell_clip_(cell_clip), proj_clip_(proj_clip) {
88 std::vector<uint32_t> inputs;
89 std::vector<std::vector<uint32_t>> input_shapes(input_shapes0);
90
91 input_shapes.push_back({n_batch, n_output});
92 input_shapes.push_back({n_batch, n_cell});
93 auto it = input_shapes.begin();
94
95 // Input and weights
96 #define AddInput(X) \
97 OperandType X##OpndTy(Type::TENSOR_FLOAT32, *it++); \
98 inputs.push_back(model_.addOperand(&X##OpndTy));
99
100 FOR_ALL_INPUT_AND_WEIGHT_TENSORS(AddInput);
101
102 #undef AddOperand
103
104 // Parameters
105 OperandType ActivationOpndTy(Type::INT32, {});
106 inputs.push_back(model_.addOperand(&ActivationOpndTy));
107 OperandType CellClipOpndTy(Type::FLOAT32, {});
108 inputs.push_back(model_.addOperand(&CellClipOpndTy));
109 OperandType ProjClipOpndTy(Type::FLOAT32, {});
110 inputs.push_back(model_.addOperand(&ProjClipOpndTy));
111
112 // Output and other intermediate state
113 std::vector<std::vector<uint32_t>> output_shapes{
114 {n_batch, n_cell * (use_cifg ? 3 : 4)},
115 {n_batch, n_output},
116 {n_batch, n_cell},
117 {n_batch, n_output},
118 };
119 std::vector<uint32_t> outputs;
120
121 auto it2 = output_shapes.begin();
122
123 #define AddOutput(X)\
124 OperandType X##OpndTy(Type::TENSOR_FLOAT32, *it2++); \
125 outputs.push_back(model_.addOperand(&X##OpndTy));
126
127 FOR_ALL_OUTPUT_TENSORS(AddOutput);
128
129 #undef AddOutput
130
131 model_.addOperation(ANEURALNETWORKS_LSTM, inputs, outputs);
132 model_.identifyInputsAndOutputs(inputs, outputs);
133
134 Input_.insert(Input_.end(), n_batch * n_input, 0.f);
135 OutputStateIn_.insert(OutputStateIn_.end(), n_batch * n_output, 0.f);
136 CellStateIn_.insert(CellStateIn_.end(), n_batch * n_cell, 0.f);
137
138 auto multiAll = [](const std::vector<uint32_t> &dims) -> uint32_t {
139 uint32_t sz = 1;
140 for(uint32_t d:dims) { sz *= d; }
141 return sz;
142 };
143
144 it2 = output_shapes.begin();
145
146 #define ReserveOutput(X) X##_.insert(X##_.end(), multiAll(*it2++), 0.f);
147
148 FOR_ALL_OUTPUT_TENSORS(ReserveOutput);
149
150 #undef ReserveOutput
151
152 model_.finish();
153 }
154
155 #define DefineSetter(X) \
156 void Set##X(const std::vector<float> &f) { \
157 X##_.insert(X##_.end(), f.begin(), f.end()); \
158 }
159
160 FOR_ALL_INPUT_AND_WEIGHT_TENSORS(DefineSetter);
161
162 #undef DefineSetter
163
ResetOutputState()164 void ResetOutputState() {
165 std::fill(OutputStateIn_.begin(), OutputStateIn_.end(), 0.f);
166 std::fill(OutputStateOut_.begin(), OutputStateOut_.end(), 0.f);
167 }
168
ResetCellState()169 void ResetCellState() {
170 std::fill(CellStateIn_.begin(), CellStateIn_.end(), 0.f);
171 std::fill(CellStateOut_.begin(), CellStateOut_.end(), 0.f);
172 }
173
SetInput(int offset,float * begin,float * end)174 void SetInput(int offset, float *begin, float *end) {
175 for (;begin != end; begin++, offset++) {
176 Input_[offset] = *begin;
177 }
178 }
179
num_inputs() const180 uint32_t num_inputs() const { return n_input_; }
num_outputs() const181 uint32_t num_outputs() const { return n_output_; }
182
GetOutput() const183 const std::vector<float> &GetOutput() const { return Output_; }
184
Invoke()185 void Invoke() {
186 ASSERT_TRUE(model_.isValid());
187
188 OutputStateIn_.swap(OutputStateOut_);
189 CellStateIn_.swap(CellStateOut_);
190
191 Compilation compilation(&model_);
192 compilation.finish();
193 Execution execution(&compilation);
194 #define SetInputOrWeight(X) \
195 ASSERT_EQ(execution.setInput(LSTMCell::k##X##Tensor, X##_.data(), \
196 sizeof(float)*X##_.size()), \
197 Result::NO_ERROR);
198
199 FOR_ALL_INPUT_AND_WEIGHT_TENSORS(SetInputOrWeight);
200
201 #undef SetInputOrWeight
202
203 #define SetOutput(X) \
204 ASSERT_EQ(execution.setOutput(LSTMCell::k##X##Tensor, X##_.data(), \
205 sizeof(float)*X##_.size()), \
206 Result::NO_ERROR);
207
208 FOR_ALL_OUTPUT_TENSORS(SetOutput);
209
210 #undef SetOutput
211
212 if (use_cifg_) {
213 execution.setInput(LSTMCell::kInputToInputWeightsTensor, nullptr, 0);
214 execution.setInput(LSTMCell::kRecurrentToInputWeightsTensor, nullptr, 0);
215 }
216
217 if (use_peephole_) {
218 if (use_cifg_) {
219 execution.setInput(LSTMCell::kCellToInputWeightsTensor, nullptr, 0);
220 }
221 } else {
222 execution.setInput(LSTMCell::kCellToInputWeightsTensor, nullptr, 0);
223 execution.setInput(LSTMCell::kCellToForgetWeightsTensor, nullptr, 0);
224 execution.setInput(LSTMCell::kCellToOutputWeightsTensor, nullptr, 0);
225 }
226
227 if (use_projection_weights_) {
228 if (!use_projection_bias_) {
229 execution.setInput(LSTMCell::kProjectionBiasTensor, nullptr, 0);
230 }
231 } else {
232 execution.setInput(LSTMCell::kProjectionWeightsTensor, nullptr, 0);
233 execution.setInput(LSTMCell::kProjectionBiasTensor, nullptr, 0);
234 }
235
236 ASSERT_EQ(execution.setInput(LSTMCell::kActivationParam,
237 &activation_, sizeof(activation_)),
238 Result::NO_ERROR);
239 ASSERT_EQ(execution.setInput(LSTMCell::kCellClipParam,
240 &cell_clip_, sizeof(cell_clip_)),
241 Result::NO_ERROR);
242 ASSERT_EQ(execution.setInput(LSTMCell::kProjClipParam,
243 &proj_clip_, sizeof(proj_clip_)),
244 Result::NO_ERROR);
245
246 ASSERT_EQ(execution.compute(), Result::NO_ERROR);
247 }
248
249 private:
250 Model model_;
251 // Execution execution_;
252 const uint32_t n_input_;
253 const uint32_t n_output_;
254
255 const bool use_cifg_;
256 const bool use_peephole_;
257 const bool use_projection_weights_;
258 const bool use_projection_bias_;
259
260 const int activation_;
261 const float cell_clip_;
262 const float proj_clip_;
263
264 #define DefineTensor(X) \
265 std::vector<float> X##_;
266
267 FOR_ALL_INPUT_AND_WEIGHT_TENSORS(DefineTensor);
268 FOR_ALL_OUTPUT_TENSORS(DefineTensor);
269
270 #undef DefineTensor
271 };
272
TEST(LSTMOpTest,BlackBoxTestNoCifgNoPeepholeNoProjectionNoClipping)273 TEST(LSTMOpTest, BlackBoxTestNoCifgNoPeepholeNoProjectionNoClipping) {
274 const int n_batch = 1;
275 const int n_input = 2;
276 // n_cell and n_output have the same size when there is no projection.
277 const int n_cell = 4;
278 const int n_output = 4;
279
280 LSTMOpModel lstm(n_batch, n_input, n_cell, n_output,
281 /*use_cifg=*/false, /*use_peephole=*/false,
282 /*use_projection_weights=*/false,
283 /*use_projection_bias=*/false,
284 /*cell_clip=*/0.0, /*proj_clip=*/0.0,
285 {
286 {n_batch, n_input}, // input tensor
287
288 {n_cell, n_input}, // input_to_input_weight tensor
289 {n_cell, n_input}, // input_to_forget_weight tensor
290 {n_cell, n_input}, // input_to_cell_weight tensor
291 {n_cell, n_input}, // input_to_output_weight tensor
292
293 {n_cell, n_output}, // recurrent_to_input_weight tensor
294 {n_cell, n_output}, // recurrent_to_forget_weight tensor
295 {n_cell, n_output}, // recurrent_to_cell_weight tensor
296 {n_cell, n_output}, // recurrent_to_output_weight tensor
297
298 {0}, // cell_to_input_weight tensor
299 {0}, // cell_to_forget_weight tensor
300 {0}, // cell_to_output_weight tensor
301
302 {n_cell}, // input_gate_bias tensor
303 {n_cell}, // forget_gate_bias tensor
304 {n_cell}, // cell_bias tensor
305 {n_cell}, // output_gate_bias tensor
306
307 {0, 0}, // projection_weight tensor
308 {0}, // projection_bias tensor
309 });
310
311 lstm.SetInputToInputWeights({-0.45018822, -0.02338299, -0.0870589,
312 -0.34550029, 0.04266912, -0.15680569,
313 -0.34856534, 0.43890524});
314
315 lstm.SetInputToCellWeights({-0.50013041, 0.1370284, 0.11810488, 0.2013163,
316 -0.20583314, 0.44344562, 0.22077113,
317 -0.29909778});
318
319 lstm.SetInputToForgetWeights({0.09701663, 0.20334584, -0.50592935,
320 -0.31343272, -0.40032279, 0.44781327,
321 0.01387155, -0.35593212});
322
323 lstm.SetInputToOutputWeights({-0.25065863, -0.28290087, 0.04613829,
324 0.40525138, 0.44272184, 0.03897077, -0.1556896,
325 0.19487578});
326
327 lstm.SetInputGateBias({0., 0., 0., 0.});
328
329 lstm.SetCellGateBias({0., 0., 0., 0.});
330
331 lstm.SetForgetGateBias({1., 1., 1., 1.});
332
333 lstm.SetOutputGateBias({0., 0., 0., 0.});
334
335 lstm.SetRecurrentToInputWeights(
336 {-0.0063535, -0.2042388, 0.31454784, -0.35746509, 0.28902304, 0.08183324,
337 -0.16555229, 0.02286911, -0.13566875, 0.03034258, 0.48091322,
338 -0.12528998, 0.24077177, -0.51332325, -0.33502164, 0.10629296});
339
340 lstm.SetRecurrentToCellWeights(
341 {-0.3407414, 0.24443203, -0.2078532, 0.26320225, 0.05695659, -0.00123841,
342 -0.4744786, -0.35869038, -0.06418842, -0.13502428, -0.501764, 0.22830659,
343 -0.46367589, 0.26016325, -0.03894562, -0.16368064});
344
345 lstm.SetRecurrentToForgetWeights(
346 {-0.48684245, -0.06655136, 0.42224967, 0.2112639, 0.27654213, 0.20864892,
347 -0.07646349, 0.45877004, 0.00141793, -0.14609534, 0.36447752, 0.09196436,
348 0.28053468, 0.01560611, -0.20127171, -0.01140004});
349
350 lstm.SetRecurrentToOutputWeights(
351 {0.43385774, -0.17194885, 0.2718237, 0.09215671, 0.24107647, -0.39835793,
352 0.18212086, 0.01301402, 0.48572797, -0.50656658, 0.20047462, -0.20607421,
353 -0.51818722, -0.15390486, 0.0468148, 0.39922136});
354
355 static float lstm_input[] = {2., 3., 3., 4., 1., 1.};
356 static float lstm_golden_output[] = {-0.02973187, 0.1229473, 0.20885126,
357 -0.15358765, -0.03716109, 0.12507336,
358 0.41193449, -0.20860538, -0.15053082,
359 0.09120187, 0.24278517, -0.12222792};
360
361 // Resetting cell_state and output_state
362 lstm.ResetCellState();
363 lstm.ResetOutputState();
364
365 const int input_sequence_size =
366 sizeof(lstm_input) / sizeof(float) / (lstm.num_inputs());
367 for (int i = 0; i < input_sequence_size; i++) {
368 float* batch0_start = lstm_input + i * lstm.num_inputs();
369 float* batch0_end = batch0_start + lstm.num_inputs();
370
371 lstm.SetInput(0, batch0_start, batch0_end);
372
373 lstm.Invoke();
374
375 float* golden_start = lstm_golden_output + i * lstm.num_outputs();
376 float* golden_end = golden_start + lstm.num_outputs();
377 std::vector<float> expected;
378 expected.insert(expected.end(), golden_start, golden_end);
379 EXPECT_THAT(lstm.GetOutput(), ElementsAreArray(ArrayFloatNear(expected)));
380 }
381 }
382
383
TEST(LSTMOpTest,BlackBoxTestWithCifgWithPeepholeNoProjectionNoClipping)384 TEST(LSTMOpTest, BlackBoxTestWithCifgWithPeepholeNoProjectionNoClipping) {
385 const int n_batch = 1;
386 const int n_input = 2;
387 // n_cell and n_output have the same size when there is no projection.
388 const int n_cell = 4;
389 const int n_output = 4;
390
391 LSTMOpModel lstm(n_batch, n_input, n_cell, n_output,
392 /*use_cifg=*/true, /*use_peephole=*/true,
393 /*use_projection_weights=*/false,
394 /*use_projection_bias=*/false,
395 /*cell_clip=*/0.0, /*proj_clip=*/0.0,
396 {
397 {n_batch, n_input}, // input tensor
398
399 {0, 0}, // input_to_input_weight tensor
400 {n_cell, n_input}, // input_to_forget_weight tensor
401 {n_cell, n_input}, // input_to_cell_weight tensor
402 {n_cell, n_input}, // input_to_output_weight tensor
403
404 {0, 0}, // recurrent_to_input_weight tensor
405 {n_cell, n_output}, // recurrent_to_forget_weight tensor
406 {n_cell, n_output}, // recurrent_to_cell_weight tensor
407 {n_cell, n_output}, // recurrent_to_output_weight tensor
408
409 {0}, // cell_to_input_weight tensor
410 {n_cell}, // cell_to_forget_weight tensor
411 {n_cell}, // cell_to_output_weight tensor
412
413 {n_cell}, // input_gate_bias tensor
414 {n_cell}, // forget_gate_bias tensor
415 {n_cell}, // cell_bias tensor
416 {n_cell}, // output_gate_bias tensor
417
418 {0, 0}, // projection_weight tensor
419 {0}, // projection_bias tensor
420 });
421
422 lstm.SetInputToCellWeights({-0.49770179, -0.27711356, -0.09624726, 0.05100781,
423 0.04717243, 0.48944736, -0.38535351,
424 -0.17212132});
425
426 lstm.SetInputToForgetWeights({-0.55291498, -0.42866567, 0.13056988,
427 -0.3633365, -0.22755712, 0.28253698, 0.24407166,
428 0.33826375});
429
430 lstm.SetInputToOutputWeights({0.10725588, -0.02335852, -0.55932593,
431 -0.09426838, -0.44257352, 0.54939759,
432 0.01533556, 0.42751634});
433
434 lstm.SetCellGateBias({0., 0., 0., 0.});
435
436 lstm.SetForgetGateBias({1., 1., 1., 1.});
437
438 lstm.SetOutputGateBias({0., 0., 0., 0.});
439
440 lstm.SetRecurrentToCellWeights(
441 {0.54066205, -0.32668582, -0.43562764, -0.56094903, 0.42957711,
442 0.01841056, -0.32764608, -0.33027974, -0.10826075, 0.20675004,
443 0.19069612, -0.03026325, -0.54532051, 0.33003211, 0.44901288,
444 0.21193194});
445
446 lstm.SetRecurrentToForgetWeights(
447 {-0.13832897, -0.0515101, -0.2359007, -0.16661474, -0.14340827,
448 0.36986142, 0.23414481, 0.55899, 0.10798943, -0.41174671, 0.17751795,
449 -0.34484994, -0.35874045, -0.11352962, 0.27268326, 0.54058349});
450
451 lstm.SetRecurrentToOutputWeights(
452 {0.41613156, 0.42610586, -0.16495961, -0.5663873, 0.30579174, -0.05115908,
453 -0.33941799, 0.23364776, 0.11178309, 0.09481031, -0.26424935, 0.46261835,
454 0.50248802, 0.26114327, -0.43736315, 0.33149987});
455
456 lstm.SetCellToForgetWeights(
457 {0.47485286, -0.51955009, -0.24458408, 0.31544167});
458 lstm.SetCellToOutputWeights(
459 {-0.17135078, 0.82760304, 0.85573703, -0.77109635});
460
461 static float lstm_input[] = {2., 3., 3., 4., 1., 1.};
462 static float lstm_golden_output[] = {-0.36444446, -0.00352185, 0.12886585,
463 -0.05163646, -0.42312205, -0.01218222,
464 0.24201041, -0.08124574, -0.358325,
465 -0.04621704, 0.21641694, -0.06471302};
466
467 // Resetting cell_state and output_state
468 lstm.ResetCellState();
469 lstm.ResetOutputState();
470
471 const int input_sequence_size =
472 sizeof(lstm_input) / sizeof(float) / (lstm.num_inputs());
473 for (int i = 0; i < input_sequence_size; i++) {
474 float* batch0_start = lstm_input + i * lstm.num_inputs();
475 float* batch0_end = batch0_start + lstm.num_inputs();
476
477 lstm.SetInput(0, batch0_start, batch0_end);
478
479 lstm.Invoke();
480
481 float* golden_start = lstm_golden_output + i * lstm.num_outputs();
482 float* golden_end = golden_start + lstm.num_outputs();
483 std::vector<float> expected;
484 expected.insert(expected.end(), golden_start, golden_end);
485 EXPECT_THAT(lstm.GetOutput(), ElementsAreArray(ArrayFloatNear(expected)));
486 }
487 }
488
TEST(LSTMOpTest,BlackBoxTestWithPeepholeWithProjectionNoClipping)489 TEST(LSTMOpTest, BlackBoxTestWithPeepholeWithProjectionNoClipping) {
490 const int n_batch = 2;
491 const int n_input = 5;
492 const int n_cell = 20;
493 const int n_output = 16;
494
495 LSTMOpModel lstm(n_batch, n_input, n_cell, n_output,
496 /*use_cifg=*/false, /*use_peephole=*/true,
497 /*use_projection_weights=*/true,
498 /*use_projection_bias=*/false,
499 /*cell_clip=*/0.0, /*proj_clip=*/0.0,
500 {
501 {n_batch, n_input}, // input tensor
502
503 {n_cell, n_input}, // input_to_input_weight tensor
504 {n_cell, n_input}, // input_to_forget_weight tensor
505 {n_cell, n_input}, // input_to_cell_weight tensor
506 {n_cell, n_input}, // input_to_output_weight tensor
507
508 {n_cell, n_output}, // recurrent_to_input_weight tensor
509 {n_cell, n_output}, // recurrent_to_forget_weight tensor
510 {n_cell, n_output}, // recurrent_to_cell_weight tensor
511 {n_cell, n_output}, // recurrent_to_output_weight tensor
512
513 {n_cell}, // cell_to_input_weight tensor
514 {n_cell}, // cell_to_forget_weight tensor
515 {n_cell}, // cell_to_output_weight tensor
516
517 {n_cell}, // input_gate_bias tensor
518 {n_cell}, // forget_gate_bias tensor
519 {n_cell}, // cell_bias tensor
520 {n_cell}, // output_gate_bias tensor
521
522 {n_output, n_cell}, // projection_weight tensor
523 {0}, // projection_bias tensor
524 });
525
526 lstm.SetInputToInputWeights(
527 {0.021393683, 0.06124551, 0.046905167, -0.014657677, -0.03149463,
528 0.09171803, 0.14647801, 0.10797193, -0.0057968358, 0.0019193048,
529 -0.2726754, 0.10154029, -0.018539885, 0.080349885, -0.10262385,
530 -0.022599787, -0.09121155, -0.008675967, -0.045206103, -0.0821282,
531 -0.008045952, 0.015478081, 0.055217247, 0.038719587, 0.044153627,
532 -0.06453243, 0.05031825, -0.046935108, -0.008164439, 0.014574226,
533 -0.1671009, -0.15519552, -0.16819797, -0.13971269, -0.11953059,
534 0.25005487, -0.22790983, 0.009855087, -0.028140958, -0.11200698,
535 0.11295408, -0.0035217577, 0.054485075, 0.05184695, 0.064711206,
536 0.10989193, 0.11674786, 0.03490607, 0.07727357, 0.11390585,
537 -0.1863375, -0.1034451, -0.13945189, -0.049401227, -0.18767063,
538 0.042483903, 0.14233552, 0.13832581, 0.18350165, 0.14545603,
539 -0.028545704, 0.024939531, 0.050929718, 0.0076203286, -0.0029723682,
540 -0.042484224, -0.11827596, -0.09171104, -0.10808628, -0.16327988,
541 -0.2273378, -0.0993647, -0.017155107, 0.0023917493, 0.049272764,
542 0.0038534778, 0.054764505, 0.089753784, 0.06947234, 0.08014476,
543 -0.04544234, -0.0497073, -0.07135631, -0.048929106, -0.004042012,
544 -0.009284026, 0.018042054, 0.0036860977, -0.07427302, -0.11434604,
545 -0.018995456, 0.031487543, 0.012834908, 0.019977754, 0.044256654,
546 -0.39292613, -0.18519334, -0.11651281, -0.06809892, 0.011373677});
547
548 lstm.SetInputToForgetWeights(
549 {-0.0018401089, -0.004852237, 0.03698424, 0.014181704, 0.028273236,
550 -0.016726194, -0.05249759, -0.10204261, 0.00861066, -0.040979505,
551 -0.009899187, 0.01923892, -0.028177269, -0.08535103, -0.14585495,
552 0.10662567, -0.01909731, -0.017883534, -0.0047269356, -0.045103323,
553 0.0030784295, 0.076784775, 0.07463696, 0.094531395, 0.0814421,
554 -0.12257899, -0.033945758, -0.031303465, 0.045630626, 0.06843887,
555 -0.13492945, -0.012480007, -0.0811829, -0.07224499, -0.09628791,
556 0.045100946, 0.0012300825, 0.013964662, 0.099372394, 0.02543059,
557 0.06958324, 0.034257296, 0.0482646, 0.06267997, 0.052625068,
558 0.12784666, 0.07077897, 0.025725935, 0.04165009, 0.07241905,
559 0.018668644, -0.037377294, -0.06277783, -0.08833636, -0.040120605,
560 -0.011405586, -0.007808335, -0.010301386, -0.005102167, 0.027717464,
561 0.05483423, 0.11449111, 0.11289652, 0.10939839, 0.13396506,
562 -0.08402166, -0.01901462, -0.044678304, -0.07720565, 0.014350063,
563 -0.11757958, -0.0652038, -0.08185733, -0.076754324, -0.092614375,
564 0.10405491, 0.052960336, 0.035755895, 0.035839386, -0.012540553,
565 0.036881298, 0.02913376, 0.03420159, 0.05448447, -0.054523353,
566 0.02582715, 0.02327355, -0.011857179, -0.0011980024, -0.034641717,
567 -0.026125094, -0.17582615, -0.15923657, -0.27486774, -0.0006143371,
568 0.0001771948, -8.470171e-05, 0.02651807, 0.045790765, 0.06956496});
569
570 lstm.SetInputToCellWeights(
571 {-0.04580283, -0.09549462, -0.032418985, -0.06454633,
572 -0.043528453, 0.043018587, -0.049152344, -0.12418144,
573 -0.078985475, -0.07596889, 0.019484362, -0.11434962,
574 -0.0074034138, -0.06314844, -0.092981495, 0.0062155537,
575 -0.025034338, -0.0028890965, 0.048929527, 0.06235075,
576 0.10665918, -0.032036792, -0.08505916, -0.10843358,
577 -0.13002433, -0.036816437, -0.02130134, -0.016518239,
578 0.0047691227, -0.0025825808, 0.066017866, 0.029991534,
579 -0.10652836, -0.1037554, -0.13056071, -0.03266643,
580 -0.033702414, -0.006473424, -0.04611692, 0.014419339,
581 -0.025174323, 0.0396852, 0.081777506, 0.06157468,
582 0.10210095, -0.009658194, 0.046511717, 0.03603906,
583 0.0069369148, 0.015960095, -0.06507666, 0.09551598,
584 0.053568836, 0.06408714, 0.12835667, -0.008714329,
585 -0.20211966, -0.12093674, 0.029450472, 0.2849013,
586 -0.029227901, 0.1164364, -0.08560263, 0.09941786,
587 -0.036999565, -0.028842626, -0.0033637602, -0.017012902,
588 -0.09720865, -0.11193351, -0.029155117, -0.017936034,
589 -0.009768936, -0.04223324, -0.036159635, 0.06505112,
590 -0.021742892, -0.023377212, -0.07221364, -0.06430552,
591 0.05453865, 0.091149814, 0.06387331, 0.007518393,
592 0.055960953, 0.069779344, 0.046411168, 0.10509911,
593 0.07463894, 0.0075130584, 0.012850982, 0.04555431,
594 0.056955688, 0.06555285, 0.050801456, -0.009862683,
595 0.00826772, -0.026555609, -0.0073611983, -0.0014897042});
596
597 lstm.SetInputToOutputWeights(
598 {-0.0998932, -0.07201956, -0.052803773, -0.15629593, -0.15001918,
599 -0.07650751, 0.02359855, -0.075155355, -0.08037709, -0.15093534,
600 0.029517552, -0.04751393, 0.010350531, -0.02664851, -0.016839722,
601 -0.023121163, 0.0077019283, 0.012851257, -0.05040649, -0.0129761,
602 -0.021737747, -0.038305793, -0.06870586, -0.01481247, -0.001285394,
603 0.10124236, 0.083122835, 0.053313006, -0.062235646, -0.075637154,
604 -0.027833903, 0.029774971, 0.1130802, 0.09218906, 0.09506135,
605 -0.086665764, -0.037162706, -0.038880914, -0.035832845, -0.014481564,
606 -0.09825003, -0.12048569, -0.097665586, -0.05287633, -0.0964047,
607 -0.11366429, 0.035777505, 0.13568819, 0.052451383, 0.050649304,
608 0.05798951, -0.021852335, -0.099848844, 0.014740475, -0.078897946,
609 0.04974699, 0.014160473, 0.06973932, 0.04964942, 0.033364646,
610 0.08190124, 0.025535367, 0.050893165, 0.048514254, 0.06945813,
611 -0.078907564, -0.06707616, -0.11844508, -0.09986688, -0.07509403,
612 0.06263226, 0.14925587, 0.20188436, 0.12098451, 0.14639415,
613 0.0015017595, -0.014267382, -0.03417257, 0.012711468, 0.0028300495,
614 -0.024758482, -0.05098548, -0.0821182, 0.014225672, 0.021544158,
615 0.08949725, 0.07505268, -0.0020780868, 0.04908258, 0.06476295,
616 -0.022907063, 0.027562456, 0.040185735, 0.019567577, -0.015598739,
617 -0.049097303, -0.017121866, -0.083368234, -0.02332002, -0.0840956});
618
619 lstm.SetInputGateBias(
620 {0.02234832, 0.14757581, 0.18176508, 0.10380666, 0.053110216,
621 -0.06928846, -0.13942584, -0.11816189, 0.19483899, 0.03652339,
622 -0.10250295, 0.036714908, -0.18426876, 0.036065217, 0.21810818,
623 0.02383196, -0.043370757, 0.08690144, -0.04444982, 0.00030581196});
624
625 lstm.SetForgetGateBias({0.035185695, -0.042891346, -0.03032477, 0.23027696,
626 0.11098921, 0.15378423, 0.09263801, 0.09790885,
627 0.09508917, 0.061199076, 0.07665568, -0.015443159,
628 -0.03499149, 0.046190713, 0.08895977, 0.10899629,
629 0.40694186, 0.06030037, 0.012413437, -0.06108739});
630
631 lstm.SetCellGateBias({-0.024379363, 0.0055531194, 0.23377132, 0.033463873,
632 -0.1483596, -0.10639995, -0.091433935, 0.058573797,
633 -0.06809782, -0.07889636, -0.043246906, -0.09829136,
634 -0.4279842, 0.034901652, 0.18797937, 0.0075234566,
635 0.016178843, 0.1749513, 0.13975595, 0.92058027});
636
637 lstm.SetOutputGateBias(
638 {0.046159424, -0.0012809046, 0.03563469, 0.12648113, 0.027195795,
639 0.35373217, -0.018957434, 0.008907322, -0.0762701, 0.12018895,
640 0.04216877, 0.0022856654, 0.040952638, 0.3147856, 0.08225149,
641 -0.057416286, -0.14995944, -0.008040261, 0.13208859, 0.029760877});
642
643 lstm.SetRecurrentToInputWeights(
644 {-0.001374326, -0.078856036, 0.10672688, 0.029162422,
645 -0.11585556, 0.02557986, -0.13446963, -0.035785314,
646 -0.01244275, 0.025961924, -0.02337298, -0.044228926,
647 -0.055839065, -0.046598054, -0.010546039, -0.06900766,
648 0.027239809, 0.022582639, -0.013296484, -0.05459212,
649 0.08981, -0.045407712, 0.08682226, -0.06867011,
650 -0.14390695, -0.02916037, 0.000996957, 0.091420636,
651 0.14283475, -0.07390571, -0.06402044, 0.062524505,
652 -0.093129106, 0.04860203, -0.08364217, -0.08119002,
653 0.009352075, 0.22920375, 0.0016303885, 0.11583097,
654 -0.13732095, 0.012405723, -0.07551853, 0.06343048,
655 0.12162708, -0.031923793, -0.014335606, 0.01790974,
656 -0.10650317, -0.0724401, 0.08554849, -0.05727212,
657 0.06556731, -0.042729504, -0.043227166, 0.011683251,
658 -0.013082158, -0.029302018, -0.010899579, -0.062036745,
659 -0.022509435, -0.00964907, -0.01567329, 0.04260106,
660 -0.07787477, -0.11576462, 0.017356863, 0.048673786,
661 -0.017577527, -0.05527947, -0.082487635, -0.040137455,
662 -0.10820036, -0.04666372, 0.022746278, -0.07851417,
663 0.01068115, 0.032956902, 0.022433773, 0.0026891115,
664 0.08944216, -0.0685835, 0.010513544, 0.07228705,
665 0.02032331, -0.059686817, -0.0005566496, -0.086984694,
666 0.040414046, -0.1380399, 0.094208956, -0.05722982,
667 0.012092817, -0.04989123, -0.086576, -0.003399834,
668 -0.04696032, -0.045747425, 0.10091314, 0.048676282,
669 -0.029037097, 0.031399418, -0.0040285117, 0.047237843,
670 0.09504992, 0.041799378, -0.049185462, -0.031518843,
671 -0.10516937, 0.026374253, 0.10058866, -0.0033195973,
672 -0.041975245, 0.0073591834, 0.0033782164, -0.004325073,
673 -0.10167381, 0.042500053, -0.01447153, 0.06464186,
674 -0.017142897, 0.03312627, 0.009205989, 0.024138335,
675 -0.011337001, 0.035530265, -0.010912711, 0.0706555,
676 -0.005894094, 0.051841937, -0.1401738, -0.02351249,
677 0.0365468, 0.07590991, 0.08838724, 0.021681072,
678 -0.10086113, 0.019608743, -0.06195883, 0.077335775,
679 0.023646897, -0.095322326, 0.02233014, 0.09756986,
680 -0.048691444, -0.009579111, 0.07595467, 0.11480546,
681 -0.09801813, 0.019894179, 0.08502348, 0.004032281,
682 0.037211012, 0.068537936, -0.048005626, -0.091520436,
683 -0.028379958, -0.01556313, 0.06554592, -0.045599163,
684 -0.01672207, -0.020169014, -0.011877351, -0.20212261,
685 0.010889619, 0.0047078193, 0.038385306, 0.08540671,
686 -0.017140968, -0.0035865551, 0.016678626, 0.005633034,
687 0.015963363, 0.00871737, 0.060130805, 0.028611384,
688 0.10109069, -0.015060172, -0.07894427, 0.06401885,
689 0.011584063, -0.024466386, 0.0047652307, -0.09041358,
690 0.030737216, -0.0046374933, 0.14215417, -0.11823516,
691 0.019899689, 0.006106124, -0.027092824, 0.0786356,
692 0.05052217, -0.058925, -0.011402121, -0.024987547,
693 -0.0013661642, -0.06832946, -0.015667673, -0.1083353,
694 -0.00096863037, -0.06988685, -0.053350925, -0.027275559,
695 -0.033664223, -0.07978348, -0.025200296, -0.017207067,
696 -0.058403496, -0.055697463, 0.005798788, 0.12965427,
697 -0.062582195, 0.0013350133, -0.10482091, 0.0379771,
698 0.072521195, -0.0029455067, -0.13797039, -0.03628521,
699 0.013806405, -0.017858358, -0.01008298, -0.07700066,
700 -0.017081132, 0.019358726, 0.0027079724, 0.004635139,
701 0.062634714, -0.02338735, -0.039547626, -0.02050681,
702 0.03385117, -0.083611414, 0.002862572, -0.09421313,
703 0.058618143, -0.08598433, 0.00972939, 0.023867095,
704 -0.053934585, -0.023203006, 0.07452513, -0.048767887,
705 -0.07314807, -0.056307215, -0.10433547, -0.06440842,
706 0.04328182, 0.04389765, -0.020006588, -0.09076438,
707 -0.11652589, -0.021705797, 0.03345259, -0.010329105,
708 -0.025767034, 0.013057034, -0.07316461, -0.10145612,
709 0.06358255, 0.18531723, 0.07759293, 0.12006465,
710 0.1305557, 0.058638252, -0.03393652, 0.09622831,
711 -0.16253184, -2.4580743e-06, 0.079869635, -0.070196845,
712 -0.005644518, 0.06857898, -0.12598175, -0.035084512,
713 0.03156317, -0.12794146, -0.031963028, 0.04692781,
714 0.030070418, 0.0071660685, -0.095516115, -0.004643372,
715 0.040170413, -0.062104587, -0.0037324072, 0.0554317,
716 0.08184801, -0.019164372, 0.06791302, 0.034257166,
717 -0.10307039, 0.021943003, 0.046745934, 0.0790918,
718 -0.0265588, -0.007824208, 0.042546265, -0.00977924,
719 -0.0002440307, -0.017384544, -0.017990116, 0.12252321,
720 -0.014512694, -0.08251313, 0.08861942, 0.13589665,
721 0.026351685, 0.012641483, 0.07466548, 0.044301085,
722 -0.045414884, -0.051112458, 0.03444247, -0.08502782,
723 -0.04106223, -0.028126027, 0.028473156, 0.10467447});
724
725 lstm.SetRecurrentToForgetWeights(
726 {-0.057784554, -0.026057621, -0.068447545, -0.022581743,
727 0.14811787, 0.10826372, 0.09471067, 0.03987225,
728 -0.0039523416, 0.00030638507, 0.053185795, 0.10572994,
729 0.08414449, -0.022036452, -0.00066928595, -0.09203576,
730 0.032950465, -0.10985798, -0.023809856, 0.0021431844,
731 -0.02196096, -0.00326074, 0.00058621005, -0.074678116,
732 -0.06193199, 0.055729095, 0.03736828, 0.020123724,
733 0.061878487, -0.04729229, 0.034919553, -0.07585433,
734 -0.04421272, -0.044019096, 0.085488975, 0.04058006,
735 -0.06890133, -0.030951202, -0.024628663, -0.07672815,
736 0.034293607, 0.08556707, -0.05293577, -0.033561368,
737 -0.04899627, 0.0241671, 0.015736353, -0.095442444,
738 -0.029564252, 0.016493602, -0.035026584, 0.022337519,
739 -0.026871363, 0.004780428, 0.0077918363, -0.03601621,
740 0.016435321, -0.03263031, -0.09543275, -0.047392778,
741 0.013454138, 0.028934088, 0.01685226, -0.086110644,
742 -0.046250615, -0.01847454, 0.047608484, 0.07339695,
743 0.034546845, -0.04881143, 0.009128804, -0.08802852,
744 0.03761666, 0.008096139, -0.014454086, 0.014361001,
745 -0.023502491, -0.0011840804, -0.07607001, 0.001856849,
746 -0.06509276, -0.006021153, -0.08570962, -0.1451793,
747 0.060212336, 0.055259194, 0.06974018, 0.049454916,
748 -0.027794661, -0.08077226, -0.016179763, 0.1169753,
749 0.17213494, -0.0056326236, -0.053934924, -0.0124349,
750 -0.11520337, 0.05409887, 0.088759385, 0.0019655675,
751 0.0042065294, 0.03881498, 0.019844765, 0.041858196,
752 -0.05695512, 0.047233116, 0.038937137, -0.06542224,
753 0.014429736, -0.09719407, 0.13908425, -0.05379757,
754 0.012321099, 0.082840554, -0.029899208, 0.044217527,
755 0.059855383, 0.07711018, -0.045319796, 0.0948846,
756 -0.011724666, -0.0033288454, -0.033542685, -0.04764985,
757 -0.13873616, 0.040668588, 0.034832682, -0.015319203,
758 -0.018715994, 0.046002675, 0.0599172, -0.043107376,
759 0.0294216, -0.002314414, -0.022424703, 0.0030315618,
760 0.0014641669, 0.0029166266, -0.11878115, 0.013738511,
761 0.12375372, -0.0006038222, 0.029104086, 0.087442465,
762 0.052958444, 0.07558703, 0.04817258, 0.044462286,
763 -0.015213451, -0.08783778, -0.0561384, -0.003008196,
764 0.047060397, -0.002058388, 0.03429439, -0.018839769,
765 0.024734668, 0.024614193, -0.042046934, 0.09597743,
766 -0.0043254104, 0.04320769, 0.0064070094, -0.0019131786,
767 -0.02558259, -0.022822596, -0.023273505, -0.02464396,
768 -0.10991725, -0.006240552, 0.0074488563, 0.024044557,
769 0.04383914, -0.046476185, 0.028658995, 0.060410924,
770 0.050786525, 0.009452605, -0.0073054377, -0.024810238,
771 0.0052906186, 0.0066939713, -0.0020913032, 0.014515517,
772 0.015898481, 0.021362653, -0.030262267, 0.016587038,
773 -0.011442813, 0.041154444, -0.007631438, -0.03423484,
774 -0.010977775, 0.036152758, 0.0066366293, 0.11915515,
775 0.02318443, -0.041350313, 0.021485701, -0.10906167,
776 -0.028218046, -0.00954771, 0.020531068, -0.11995105,
777 -0.03672871, 0.024019798, 0.014255957, -0.05221243,
778 -0.00661567, -0.04630967, 0.033188973, 0.10107534,
779 -0.014027541, 0.030796422, -0.10270911, -0.035999842,
780 0.15443139, 0.07684145, 0.036571592, -0.035900835,
781 -0.0034699554, 0.06209149, 0.015920248, -0.031122351,
782 -0.03858649, 0.01849943, 0.13872518, 0.01503974,
783 0.069941424, -0.06948533, -0.0088794185, 0.061282158,
784 -0.047401894, 0.03100163, -0.041533746, -0.10430945,
785 0.044574402, -0.01425562, -0.024290353, 0.034563623,
786 0.05866852, 0.023947537, -0.09445152, 0.035450947,
787 0.02247216, -0.0042998926, 0.061146557, -0.10250651,
788 0.020881841, -0.06747029, 0.10062043, -0.0023941975,
789 0.03532124, -0.016341697, 0.09685456, -0.016764693,
790 0.051808182, 0.05875331, -0.04536488, 0.001626336,
791 -0.028892258, -0.01048663, -0.009793449, -0.017093895,
792 0.010987891, 0.02357273, -0.00010856845, 0.0099760275,
793 -0.001845119, -0.03551521, 0.0018358806, 0.05763657,
794 -0.01769146, 0.040995963, 0.02235177, -0.060430344,
795 0.11475477, -0.023854522, 0.10071741, 0.0686208,
796 -0.014250481, 0.034261297, 0.047418304, 0.08562733,
797 -0.030519066, 0.0060542435, 0.014653856, -0.038836084,
798 0.04096551, 0.032249358, -0.08355519, -0.026823482,
799 0.056386515, -0.010401743, -0.028396193, 0.08507674,
800 0.014410365, 0.020995233, 0.17040324, 0.11511526,
801 0.02459721, 0.0066619175, 0.025853224, -0.023133837,
802 -0.081302024, 0.017264642, -0.009585969, 0.09491168,
803 -0.051313367, 0.054532815, -0.014298593, 0.10657464,
804 0.007076659, 0.10964551, 0.0409152, 0.008275321,
805 -0.07283536, 0.07937492, 0.04192024, -0.1075027});
806
807 lstm.SetRecurrentToCellWeights(
808 {-0.037322544, 0.018592842, 0.0056175636, -0.06253426,
809 0.055647098, -0.05713207, -0.05626563, 0.005559383,
810 0.03375411, -0.025757805, -0.088049285, 0.06017052,
811 -0.06570978, 0.007384076, 0.035123326, -0.07920549,
812 0.053676967, 0.044480428, -0.07663568, 0.0071805613,
813 0.08089997, 0.05143358, 0.038261272, 0.03339287,
814 -0.027673481, 0.044746667, 0.028349208, 0.020090483,
815 -0.019443132, -0.030755889, -0.0040000007, 0.04465846,
816 -0.021585021, 0.0031670958, 0.0053199246, -0.056117613,
817 -0.10893326, 0.076739706, -0.08509834, -0.027997585,
818 0.037871376, 0.01449768, -0.09002357, -0.06111149,
819 -0.046195522, 0.0422062, -0.005683705, -0.1253618,
820 -0.012925729, -0.04890792, 0.06985068, 0.037654128,
821 0.03398274, -0.004781977, 0.007032333, -0.031787455,
822 0.010868644, -0.031489216, 0.09525667, 0.013939797,
823 0.0058680447, 0.0167067, 0.02668468, -0.04797466,
824 -0.048885044, -0.12722108, 0.035304096, 0.06554885,
825 0.00972396, -0.039238118, -0.05159735, -0.11329045,
826 0.1613692, -0.03750952, 0.06529313, -0.071974665,
827 -0.11769596, 0.015524369, -0.0013754242, -0.12446318,
828 0.02786344, -0.014179351, 0.005264273, 0.14376344,
829 0.015983658, 0.03406988, -0.06939408, 0.040699873,
830 0.02111075, 0.09669095, 0.041345075, -0.08316494,
831 -0.07684199, -0.045768797, 0.032298047, -0.041805092,
832 0.0119405, 0.0061010392, 0.12652606, 0.0064572375,
833 -0.024950314, 0.11574242, 0.04508852, -0.04335324,
834 0.06760663, -0.027437469, 0.07216407, 0.06977076,
835 -0.05438599, 0.034033038, -0.028602652, 0.05346137,
836 0.043184172, -0.037189785, 0.10420091, 0.00882477,
837 -0.054019816, -0.074273005, -0.030617684, -0.0028467078,
838 0.024302477, -0.0038869337, 0.005332455, 0.0013399826,
839 0.04361412, -0.007001822, 0.09631092, -0.06702025,
840 -0.042049985, -0.035070654, -0.04103342, -0.10273396,
841 0.0544271, 0.037184782, -0.13150354, -0.0058036847,
842 -0.008264958, 0.042035464, 0.05891794, 0.029673764,
843 0.0063542654, 0.044788733, 0.054816857, 0.062257513,
844 -0.00093483756, 0.048938446, -0.004952862, -0.007730018,
845 -0.04043371, -0.017094059, 0.07229206, -0.023670016,
846 -0.052195564, -0.025616996, -0.01520939, 0.045104615,
847 -0.007376126, 0.003533447, 0.006570588, 0.056037236,
848 0.12436656, 0.051817212, 0.028532185, -0.08686856,
849 0.11868599, 0.07663395, -0.07323171, 0.03463402,
850 -0.050708205, -0.04458982, -0.11590894, 0.021273347,
851 0.1251325, -0.15313013, -0.12224372, 0.17228661,
852 0.023029093, 0.086124025, 0.006445803, -0.03496501,
853 0.028332196, 0.04449512, -0.042436164, -0.026587414,
854 -0.006041347, -0.09292539, -0.05678812, 0.03897832,
855 0.09465633, 0.008115513, -0.02171956, 0.08304309,
856 0.071401566, 0.019622514, 0.032163795, -0.004167056,
857 0.02295182, 0.030739572, 0.056506045, 0.004612461,
858 0.06524936, 0.059999723, 0.046395954, -0.0045512207,
859 -0.1335546, -0.030136576, 0.11584653, -0.014678886,
860 0.0020118146, -0.09688814, -0.0790206, 0.039770417,
861 -0.0329582, 0.07922767, 0.029322514, 0.026405897,
862 0.04207835, -0.07073373, 0.063781224, 0.0859677,
863 -0.10925287, -0.07011058, 0.048005477, 0.03438226,
864 -0.09606514, -0.006669445, -0.043381985, 0.04240257,
865 -0.06955775, -0.06769346, 0.043903265, -0.026784198,
866 -0.017840602, 0.024307009, -0.040079936, -0.019946516,
867 0.045318738, -0.12233574, 0.026170589, 0.0074471775,
868 0.15978073, 0.10185836, 0.10298046, -0.015476589,
869 -0.039390966, -0.072174534, 0.0739445, -0.1211869,
870 -0.0347889, -0.07943156, 0.014809798, -0.12412325,
871 -0.0030663363, 0.039695457, 0.0647603, -0.08291318,
872 -0.018529687, -0.004423833, 0.0037507233, 0.084633216,
873 -0.01514876, -0.056505352, -0.012800942, -0.06994386,
874 0.012962922, -0.031234352, 0.07029052, 0.016418684,
875 0.03618972, 0.055686004, -0.08663945, -0.017404709,
876 -0.054761406, 0.029065743, 0.052404847, 0.020238016,
877 0.0048197987, -0.0214882, 0.07078733, 0.013016777,
878 0.06262858, 0.009184685, 0.020785125, -0.043904778,
879 -0.0270329, -0.03299152, -0.060088247, -0.015162964,
880 -0.001828936, 0.12642565, -0.056757294, 0.013586685,
881 0.09232601, -0.035886683, 0.06000002, 0.05229691,
882 -0.052580316, -0.082029596, -0.010794592, 0.012947712,
883 -0.036429964, -0.085508935, -0.13127148, -0.017744139,
884 0.031502828, 0.036232427, -0.031581745, 0.023051167,
885 -0.05325106, -0.03421577, 0.028793324, -0.034633752,
886 -0.009881397, -0.043551125, -0.018609839, 0.0019097115,
887 -0.008799762, 0.056595087, 0.0022273948, 0.055752404});
888
889 lstm.SetRecurrentToOutputWeights({
890 0.025825322, -0.05813119, 0.09495884, -0.045984812, -0.01255415,
891 -0.0026479573, -0.08196161, -0.054914974, -0.0046604523, -0.029587349,
892 -0.044576716, -0.07480124, -0.082868785, 0.023254942, 0.027502948,
893 -0.0039728214, -0.08683098, -0.08116779, -0.014675607, -0.037924774,
894 -0.023314456, -0.007401714, -0.09255757, 0.029460307, -0.08829125,
895 -0.005139627, -0.08989442, -0.0555066, 0.13596267, -0.025062224,
896 -0.048351806, -0.03850004, 0.07266485, -0.022414139, 0.05940088,
897 0.075114764, 0.09597592, -0.010211725, -0.0049794707, -0.011523867,
898 -0.025980417, 0.072999895, 0.11091378, -0.081685916, 0.014416728,
899 0.043229222, 0.034178585, -0.07530371, 0.035837382, -0.085607,
900 -0.007721233, -0.03287832, -0.043848954, -0.06404588, -0.06632928,
901 -0.073643476, 0.008214239, -0.045984086, 0.039764922, 0.03474462,
902 0.060612556, -0.080590084, 0.049127717, 0.04151091, -0.030063879,
903 0.008801774, -0.023021035, -0.019558564, 0.05158114, -0.010947698,
904 -0.011825728, 0.0075720972, 0.0699727, -0.0039981045, 0.069350146,
905 0.08799282, 0.016156472, 0.035502106, 0.11695009, 0.006217345,
906 0.13392477, -0.037875112, 0.025745004, 0.08940699, -0.00924166,
907 0.0046702605, -0.036598757, -0.08811812, 0.10522024, -0.032441203,
908 0.008176899, -0.04454919, 0.07058152, 0.0067963637, 0.039206743,
909 0.03259838, 0.03725492, -0.09515802, 0.013326398, -0.052055415,
910 -0.025676316, 0.03198509, -0.015951829, -0.058556724, 0.036879618,
911 0.043357447, 0.028362012, -0.05908629, 0.0059240665, -0.04995891,
912 -0.019187413, 0.0276265, -0.01628143, 0.0025863599, 0.08800015,
913 0.035250366, -0.022165963, -0.07328642, -0.009415526, -0.07455109,
914 0.11690406, 0.0363299, 0.07411125, 0.042103454, -0.009660886,
915 0.019076364, 0.018299393, -0.046004917, 0.08891175, 0.0431396,
916 -0.026327137, -0.051502608, 0.08979574, -0.051670972, 0.04940282,
917 -0.07491107, -0.021240504, 0.022596184, -0.034280192, 0.060163025,
918 -0.058211457, -0.051837247, -0.01349775, -0.04639988, -0.035936575,
919 -0.011681591, 0.064818054, 0.0073146066, -0.021745546, -0.043124277,
920 -0.06471268, -0.07053354, -0.029321948, -0.05330136, 0.016933719,
921 -0.053782392, 0.13747959, -0.1361751, -0.11569455, 0.0033329215,
922 0.05693899, -0.053219706, 0.063698, 0.07977434, -0.07924483,
923 0.06936997, 0.0034815092, -0.007305279, -0.037325785, -0.07251102,
924 -0.033633437, -0.08677009, 0.091591336, -0.14165086, 0.021752775,
925 0.019683983, 0.0011612234, -0.058154266, 0.049996935, 0.0288841,
926 -0.0024567875, -0.14345716, 0.010955264, -0.10234828, 0.1183656,
927 -0.0010731248, -0.023590032, -0.072285876, -0.0724771, -0.026382286,
928 -0.0014920527, 0.042667855, 0.0018776858, 0.02986552, 0.009814309,
929 0.0733756, 0.12289186, 0.018043943, -0.0458958, 0.049412545,
930 0.033632483, 0.05495232, 0.036686596, -0.013781798, -0.010036754,
931 0.02576849, -0.08307328, 0.010112348, 0.042521734, -0.05869831,
932 -0.071689695, 0.03876447, -0.13275425, -0.0352966, -0.023077697,
933 0.10285965, 0.084736146, 0.15568255, -0.00040734606, 0.027835453,
934 -0.10292561, -0.032401145, 0.10053256, -0.026142767, -0.08271222,
935 -0.0030240538, -0.016368777, 0.1070414, 0.042672627, 0.013456989,
936 -0.0437609, -0.022309763, 0.11576483, 0.04108048, 0.061026827,
937 -0.0190714, -0.0869359, 0.037901703, 0.0610107, 0.07202949,
938 0.01675338, 0.086139716, -0.08795751, -0.014898893, -0.023771819,
939 -0.01965048, 0.007955471, -0.043740474, 0.03346837, -0.10549954,
940 0.090567775, 0.042013682, -0.03176985, 0.12569028, -0.02421228,
941 -0.029526481, 0.023851605, 0.031539805, 0.05292009, -0.02344001,
942 -0.07811758, -0.08834428, 0.10094801, 0.16594367, -0.06861939,
943 -0.021256343, -0.041093912, -0.06669611, 0.035498552, 0.021757556,
944 -0.09302526, -0.015403468, -0.06614931, -0.051798206, -0.013874718,
945 0.03630673, 0.010412845, -0.08077351, 0.046185967, 0.0035662893,
946 0.03541868, -0.094149634, -0.034814864, 0.003128424, -0.020674974,
947 -0.03944324, -0.008110165, -0.11113267, 0.08484226, 0.043586485,
948 0.040582247, 0.0968012, -0.065249965, -0.028036479, 0.0050708856,
949 0.0017462453, 0.0326779, 0.041296225, 0.09164146, -0.047743853,
950 -0.015952192, -0.034451712, 0.084197424, -0.05347844, -0.11768019,
951 0.085926116, -0.08251791, -0.045081906, 0.0948852, 0.068401024,
952 0.024856757, 0.06978981, -0.057309967, -0.012775832, -0.0032452994,
953 0.01977615, -0.041040014, -0.024264973, 0.063464895, 0.05431621,
954 });
955
956 lstm.SetCellToInputWeights(
957 {0.040369894, 0.030746894, 0.24704495, 0.018586371, -0.037586458,
958 -0.15312155, -0.11812848, -0.11465643, 0.20259799, 0.11418174,
959 -0.10116027, -0.011334949, 0.12411352, -0.076769054, -0.052169047,
960 0.21198851, -0.38871562, -0.09061183, -0.09683246, -0.21929175});
961
962 lstm.SetCellToForgetWeights(
963 {-0.01998659, -0.15568835, -0.24248174, -0.012770197, 0.041331276,
964 -0.072311886, -0.052123554, -0.0066330447, -0.043891653, 0.036225766,
965 -0.047248036, 0.021479502, 0.033189066, 0.11952997, -0.020432774,
966 0.64658105, -0.06650122, -0.03467612, 0.095340036, 0.23647355});
967
968 lstm.SetCellToOutputWeights(
969 {0.08286371, -0.08261836, -0.51210177, 0.002913762, 0.17764764,
970 -0.5495371, -0.08460716, -0.24552552, 0.030037103, 0.04123544,
971 -0.11940523, 0.007358328, 0.1890978, 0.4833202, -0.34441817,
972 0.36312827, -0.26375428, 0.1457655, -0.19724406, 0.15548733});
973
974 lstm.SetProjectionWeights(
975 {-0.009802181, 0.09401916, 0.0717386, -0.13895074, 0.09641832,
976 0.060420845, 0.08539281, 0.054285463, 0.061395317, 0.034448683,
977 -0.042991187, 0.019801661, -0.16840284, -0.015726732, -0.23041931,
978 -0.024478018, -0.10959692, -0.013875541, 0.18600968, -0.061274476,
979 0.0138165, -0.08160894, -0.07661644, 0.032372914, 0.16169067,
980 0.22465782, -0.03993472, -0.004017731, 0.08633481, -0.28869787,
981 0.08682067, 0.17240396, 0.014975425, 0.056431185, 0.031037588,
982 0.16702051, 0.0077946745, 0.15140012, 0.29405436, 0.120285,
983 -0.188994, -0.027265169, 0.043389652, -0.022061434, 0.014777949,
984 -0.20203483, 0.094781205, 0.19100232, 0.13987629, -0.036132768,
985 -0.06426278, -0.05108664, 0.13221376, 0.009441198, -0.16715929,
986 0.15859416, -0.040437475, 0.050779544, -0.022187516, 0.012166504,
987 0.027685808, -0.07675938, -0.0055694645, -0.09444123, 0.0046453946,
988 0.050794356, 0.10770313, -0.20790008, -0.07149004, -0.11425117,
989 0.008225835, -0.035802525, 0.14374903, 0.15262283, 0.048710253,
990 0.1847461, -0.007487823, 0.11000021, -0.09542012, 0.22619456,
991 -0.029149994, 0.08527916, 0.009043713, 0.0042746216, 0.016261552,
992 0.022461696, 0.12689082, -0.043589946, -0.12035478, -0.08361797,
993 -0.050666027, -0.1248618, -0.1275799, -0.071875185, 0.07377272,
994 0.09944291, -0.18897448, -0.1593054, -0.06526116, -0.040107165,
995 -0.004618631, -0.067624845, -0.007576253, 0.10727444, 0.041546922,
996 -0.20424393, 0.06907816, 0.050412357, 0.00724631, 0.039827548,
997 0.12449835, 0.10747581, 0.13708383, 0.09134148, -0.12617786,
998 -0.06428341, 0.09956831, 0.1208086, -0.14676677, -0.0727722,
999 0.1126304, 0.010139365, 0.015571211, -0.038128063, 0.022913318,
1000 -0.042050496, 0.16842307, -0.060597885, 0.10531834, -0.06411776,
1001 -0.07451711, -0.03410368, -0.13393489, 0.06534304, 0.003620307,
1002 0.04490757, 0.05970546, 0.05197996, 0.02839995, 0.10434969,
1003 -0.013699693, -0.028353551, -0.07260381, 0.047201227, -0.024575593,
1004 -0.036445823, 0.07155557, 0.009672501, -0.02328883, 0.009533515,
1005 -0.03606021, -0.07421458, -0.028082801, -0.2678904, -0.13221288,
1006 0.18419984, -0.13012612, -0.014588381, -0.035059117, -0.04824723,
1007 0.07830115, -0.056184657, 0.03277091, 0.025466874, 0.14494097,
1008 -0.12522776, -0.098633975, -0.10766018, -0.08317623, 0.08594209,
1009 0.07749552, 0.039474737, 0.1776665, -0.07409566, -0.0477268,
1010 0.29323658, 0.10801441, 0.1154011, 0.013952499, 0.10739139,
1011 0.10708251, -0.051456142, 0.0074137426, -0.10430189, 0.10034707,
1012 0.045594677, 0.0635285, -0.0715442, -0.089667566, -0.10811871,
1013 0.00026344223, 0.08298446, -0.009525053, 0.006585689, -0.24567553,
1014 -0.09450807, 0.09648481, 0.026996298, -0.06419476, -0.04752702,
1015 -0.11063944, -0.23441927, -0.17608605, -0.052156363, 0.067035615,
1016 0.19271925, -0.0032889997, -0.043264326, 0.09663576, -0.057112187,
1017 -0.10100678, 0.0628376, 0.04447668, 0.017961001, -0.10094388,
1018 -0.10190601, 0.18335468, 0.10494553, -0.052095775, -0.0026118709,
1019 0.10539724, -0.04383912, -0.042349473, 0.08438151, -0.1947263,
1020 0.02251204, 0.11216432, -0.10307853, 0.17351969, -0.039091777,
1021 0.08066188, -0.00561982, 0.12633002, 0.11335965, -0.0088127935,
1022 -0.019777594, 0.06864014, -0.059751723, 0.016233567, -0.06894641,
1023 -0.28651384, -0.004228674, 0.019708522, -0.16305895, -0.07468996,
1024 -0.0855457, 0.099339016, -0.07580735, -0.13775392, 0.08434318,
1025 0.08330512, -0.12131499, 0.031935584, 0.09180414, -0.08876437,
1026 -0.08049874, 0.008753825, 0.03498998, 0.030215185, 0.03907079,
1027 0.089751154, 0.029194152, -0.03337423, -0.019092513, 0.04331237,
1028 0.04299654, -0.036394123, -0.12915532, 0.09793732, 0.07512415,
1029 -0.11319543, -0.032502122, 0.15661901, 0.07671967, -0.005491124,
1030 -0.19379048, -0.218606, 0.21448623, 0.017840758, 0.1416943,
1031 -0.07051762, 0.19488361, 0.02664691, -0.18104725, -0.09334311,
1032 0.15026465, -0.15493552, -0.057762887, -0.11604192, -0.262013,
1033 -0.01391798, 0.012185008, 0.11156489, -0.07483202, 0.06693364,
1034 -0.26151478, 0.046425626, 0.036540434, -0.16435726, 0.17338543,
1035 -0.21401681, -0.11385144, -0.08283257, -0.069031075, 0.030635102,
1036 0.010969227, 0.11109743, 0.010919218, 0.027526086, 0.13519906,
1037 0.01891392, -0.046839405, -0.040167913, 0.017953383, -0.09700955,
1038 0.0061885654, -0.07000971, 0.026893595, -0.038844477, 0.14543656});
1039
1040 static float lstm_input[][20] = {
1041 {// Batch0: 4 (input_sequence_size) * 5 (n_input)
1042 0.787926, 0.151646, 0.071352, 0.118426, 0.458058, 0.596268, 0.998386,
1043 0.568695, 0.864524, 0.571277, 0.073204, 0.296072, 0.743333, 0.069199,
1044 0.045348, 0.867394, 0.291279, 0.013714, 0.482521, 0.626339},
1045
1046 {// Batch1: 4 (input_sequence_size) * 5 (n_input)
1047 0.295743, 0.544053, 0.690064, 0.858138, 0.497181, 0.642421, 0.524260,
1048 0.134799, 0.003639, 0.162482, 0.640394, 0.930399, 0.050782, 0.432485,
1049 0.988078, 0.082922, 0.563329, 0.865614, 0.333232, 0.259916}};
1050
1051 static float lstm_golden_output[][64] = {
1052 {// Batch0: 4 (input_sequence_size) * 16 (n_output)
1053 -0.00396806, 0.029352, -0.00279226, 0.0159977, -0.00835576,
1054 -0.0211779, 0.0283512, -0.0114597, 0.00907307, -0.0244004,
1055 -0.0152191, -0.0259063, 0.00914318, 0.00415118, 0.017147,
1056 0.0134203, -0.0166936, 0.0381209, 0.000889694, 0.0143363,
1057 -0.0328911, -0.0234288, 0.0333051, -0.012229, 0.0110322,
1058 -0.0457725, -0.000832209, -0.0202817, 0.0327257, 0.0121308,
1059 0.0155969, 0.0312091, -0.0213783, 0.0350169, 0.000324794,
1060 0.0276012, -0.0263374, -0.0371449, 0.0446149, -0.0205474,
1061 0.0103729, -0.0576349, -0.0150052, -0.0292043, 0.0376827,
1062 0.0136115, 0.0243435, 0.0354492, -0.0189322, 0.0464512,
1063 -0.00251373, 0.0225745, -0.0308346, -0.0317124, 0.0460407,
1064 -0.0189395, 0.0149363, -0.0530162, -0.0150767, -0.0340193,
1065 0.0286833, 0.00824207, 0.0264887, 0.0305169},
1066 {// Batch1: 4 (input_sequence_size) * 16 (n_output)
1067 -0.013869, 0.0287268, -0.00334693, 0.00733398, -0.0287926,
1068 -0.0186926, 0.0193662, -0.0115437, 0.00422612, -0.0345232,
1069 0.00223253, -0.00957321, 0.0210624, 0.013331, 0.0150954,
1070 0.02168, -0.0141913, 0.0322082, 0.00227024, 0.0260507,
1071 -0.0188721, -0.0296489, 0.0399134, -0.0160509, 0.0116039,
1072 -0.0447318, -0.0150515, -0.0277406, 0.0316596, 0.0118233,
1073 0.0214762, 0.0293641, -0.0204549, 0.0450315, -0.00117378,
1074 0.0167673, -0.0375007, -0.0238314, 0.038784, -0.0174034,
1075 0.0131743, -0.0506589, -0.0048447, -0.0240239, 0.0325789,
1076 0.00790065, 0.0220157, 0.0333314, -0.0264787, 0.0387855,
1077 -0.000764675, 0.0217599, -0.037537, -0.0335206, 0.0431679,
1078 -0.0211424, 0.010203, -0.062785, -0.00832363, -0.025181,
1079 0.0412031, 0.0118723, 0.0239643, 0.0394009}};
1080
1081 // Resetting cell_state and output_state
1082 lstm.ResetCellState();
1083 lstm.ResetOutputState();
1084
1085 const int input_sequence_size =
1086 sizeof(lstm_input[0]) / sizeof(float) / (lstm.num_inputs());
1087 for (int i = 0; i < input_sequence_size; i++) {
1088 float* batch0_start = lstm_input[0] + i * lstm.num_inputs();
1089 float* batch0_end = batch0_start + lstm.num_inputs();
1090
1091 lstm.SetInput(0, batch0_start, batch0_end);
1092
1093 float* batch1_start = lstm_input[1] + i * lstm.num_inputs();
1094 float* batch1_end = batch1_start + lstm.num_inputs();
1095 lstm.SetInput(lstm.num_inputs(), batch1_start, batch1_end);
1096
1097 lstm.Invoke();
1098
1099 float* golden_start_batch0 = lstm_golden_output[0] + i * lstm.num_outputs();
1100 float* golden_end_batch0 = golden_start_batch0 + lstm.num_outputs();
1101 float* golden_start_batch1 = lstm_golden_output[1] + i * lstm.num_outputs();
1102 float* golden_end_batch1 = golden_start_batch1 + lstm.num_outputs();
1103 std::vector<float> expected;
1104 expected.insert(expected.end(), golden_start_batch0, golden_end_batch0);
1105 expected.insert(expected.end(), golden_start_batch1, golden_end_batch1);
1106 EXPECT_THAT(lstm.GetOutput(), ElementsAreArray(ArrayFloatNear(expected)));
1107 }
1108 }
1109
1110
1111 } // namespace wrapper
1112 } // namespace nn
1113 } // namespace android
1114