2 # Copyright (C) 2019 The Android Open Source Project
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
8 # http://www.apache.org/licenses/LICENSE-2.0
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.
17 # Unidirectional Sequence LSTM Test:
18 # 4 Time Step, No Layer Normalization, No Cifg, Peephole, Projection w/ Bias, and No Clipping.
19 # Adapted from TFLite's NoCifgPeepholeProjectionAndBiasClippingLstmTest.LstmBlackBoxTest.
30 input = Input("input", "TENSOR_FLOAT32", "{%d, %d, %d}" % (n_batch, max_time, n_input))
32 input_to_input_weights = Input("input_to_input_weights", "TENSOR_FLOAT32",
33 "{%d, %d}" % (n_cell, n_input))
34 input_to_forget_weights = Input("input_to_forget_weights", "TENSOR_FLOAT32",
35 "{%d, %d}" % (n_cell, n_input))
36 input_to_cell_weights = Input("input_to_cell_weights", "TENSOR_FLOAT32",
37 "{%d, %d}" % (n_cell, n_input))
38 input_to_output_weights = Input("input_to_output_weights", "TENSOR_FLOAT32",
39 "{%d, %d}" % (n_cell, n_input))
41 recurrent_to_input_weights = Input("recurrent_to_intput_weights",
43 "{%d, %d}" % (n_cell, n_output))
44 recurrent_to_forget_weights = Input("recurrent_to_forget_weights",
46 "{%d, %d}" % (n_cell, n_output))
47 recurrent_to_cell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT32",
48 "{%d, %d}" % (n_cell, n_output))
49 recurrent_to_output_weights = Input("recurrent_to_output_weights",
51 "{%d, %d}" % (n_cell, n_output))
53 cell_to_input_weights = Input("cell_to_input_weights", "TENSOR_FLOAT32",
55 cell_to_forget_weights = Input("cell_to_forget_weights", "TENSOR_FLOAT32",
57 cell_to_output_weights = Input("cell_to_output_weights", "TENSOR_FLOAT32",
60 input_gate_bias = Input("input_gate_bias", "TENSOR_FLOAT32", "{%d}" % (n_cell))
61 forget_gate_bias = Input("forget_gate_bias", "TENSOR_FLOAT32",
63 cell_gate_bias = Input("cell_gate_bias", "TENSOR_FLOAT32", "{%d}" % (n_cell))
64 output_gate_bias = Input("output_gate_bias", "TENSOR_FLOAT32",
67 projection_weights = Input("projection_weights", "TENSOR_FLOAT32",
68 "{%d,%d}" % (n_output, n_cell))
69 projection_bias = Input("projection_bias", "TENSOR_FLOAT32", "{%d}" % n_output)
71 output_state_in = Input("output_state_in", "TENSOR_FLOAT32",
72 "{%d, %d}" % (n_batch, n_output))
73 cell_state_in = Input("cell_state_in", "TENSOR_FLOAT32",
74 "{%d, %d}" % (n_batch, n_cell))
76 activation_param = Int32Scalar("activation_param", 4) # Tanh
77 cell_clip_param = Float32Scalar("cell_clip_param", 0.)
78 proj_clip_param = Float32Scalar("proj_clip_param", 0.)
79 time_major_param = BoolScalar("time_major_param", False)
81 input_layer_norm_weights = Input("input_layer_norm_weights", "TENSOR_FLOAT32", "{0}")
82 forget_layer_norm_weights = Input("forget_layer_norm_weights", "TENSOR_FLOAT32", "{0}")
83 cell_layer_norm_weights = Input("cell_layer_norm_weights", "TENSOR_FLOAT32", "{0}")
84 output_layer_norm_weights = Input("output_layer_norm_weights", "TENSOR_FLOAT32", "{0}")
86 output = Output("output", "TENSOR_FLOAT32", "{%d, %d, %d}" % (n_batch, max_time, n_output))
88 model = model.Operation(
89 "UNIDIRECTIONAL_SEQUENCE_LSTM", input, input_to_input_weights, input_to_forget_weights,
90 input_to_cell_weights, input_to_output_weights, recurrent_to_input_weights,
91 recurrent_to_forget_weights, recurrent_to_cell_weights,
92 recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights,
93 cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias,
94 output_gate_bias, projection_weights, projection_bias, output_state_in,
95 cell_state_in, activation_param, cell_clip_param, proj_clip_param, time_major_param,
96 input_layer_norm_weights, forget_layer_norm_weights,
97 cell_layer_norm_weights, output_layer_norm_weights).To([output])
99 # Example 1. Input in operand 0,
101 input_to_input_weights: [
102 0.021393683, 0.06124551, 0.046905167, -0.014657677, -0.03149463,
103 0.09171803, 0.14647801, 0.10797193, -0.0057968358, 0.0019193048,
104 -0.2726754, 0.10154029, -0.018539885, 0.080349885, -0.10262385,
105 -0.022599787, -0.09121155, -0.008675967, -0.045206103, -0.0821282,
106 -0.008045952, 0.015478081, 0.055217247, 0.038719587, 0.044153627,
107 -0.06453243, 0.05031825, -0.046935108, -0.008164439, 0.014574226,
108 -0.1671009, -0.15519552, -0.16819797, -0.13971269, -0.11953059,
109 0.25005487, -0.22790983, 0.009855087, -0.028140958, -0.11200698,
110 0.11295408, -0.0035217577, 0.054485075, 0.05184695, 0.064711206,
111 0.10989193, 0.11674786, 0.03490607, 0.07727357, 0.11390585,
112 -0.1863375, -0.1034451, -0.13945189, -0.049401227, -0.18767063,
113 0.042483903, 0.14233552, 0.13832581, 0.18350165, 0.14545603,
114 -0.028545704, 0.024939531, 0.050929718, 0.0076203286, -0.0029723682,
115 -0.042484224, -0.11827596, -0.09171104, -0.10808628, -0.16327988,
116 -0.2273378, -0.0993647, -0.017155107, 0.0023917493, 0.049272764,
117 0.0038534778, 0.054764505, 0.089753784, 0.06947234, 0.08014476,
118 -0.04544234, -0.0497073, -0.07135631, -0.048929106, -0.004042012,
119 -0.009284026, 0.018042054, 0.0036860977, -0.07427302, -0.11434604,
120 -0.018995456, 0.031487543, 0.012834908, 0.019977754, 0.044256654,
121 -0.39292613, -0.18519334, -0.11651281, -0.06809892, 0.011373677,
123 input_to_forget_weights: [
124 -0.0018401089, -0.004852237, 0.03698424, 0.014181704,
125 0.028273236, -0.016726194, -0.05249759, -0.10204261,
126 0.00861066, -0.040979505, -0.009899187, 0.01923892,
127 -0.028177269, -0.08535103, -0.14585495, 0.10662567,
128 -0.01909731, -0.017883534, -0.0047269356, -0.045103323,
129 0.0030784295, 0.076784775, 0.07463696, 0.094531395,
130 0.0814421, -0.12257899, -0.033945758, -0.031303465,
131 0.045630626, 0.06843887, -0.13492945, -0.012480007,
132 -0.0811829, -0.07224499, -0.09628791, 0.045100946,
133 0.0012300825, 0.013964662, 0.099372394, 0.02543059,
134 0.06958324, 0.034257296, 0.0482646, 0.06267997,
135 0.052625068, 0.12784666, 0.07077897, 0.025725935,
136 0.04165009, 0.07241905, 0.018668644, -0.037377294,
137 -0.06277783, -0.08833636, -0.040120605, -0.011405586,
138 -0.007808335, -0.010301386, -0.005102167, 0.027717464,
139 0.05483423, 0.11449111, 0.11289652, 0.10939839,
140 0.13396506, -0.08402166, -0.01901462, -0.044678304,
141 -0.07720565, 0.014350063, -0.11757958, -0.0652038,
142 -0.08185733, -0.076754324, -0.092614375, 0.10405491,
143 0.052960336, 0.035755895, 0.035839386, -0.012540553,
144 0.036881298, 0.02913376, 0.03420159, 0.05448447,
145 -0.054523353, 0.02582715, 0.02327355, -0.011857179,
146 -0.0011980024, -0.034641717, -0.026125094, -0.17582615,
147 -0.15923657, -0.27486774, -0.0006143371, 0.0001771948,
148 -8.470171e-05, 0.02651807, 0.045790765, 0.06956496,
150 input_to_cell_weights: [
151 -0.04580283, -0.09549462, -0.032418985, -0.06454633,
152 -0.043528453, 0.043018587, -0.049152344, -0.12418144,
153 -0.078985475, -0.07596889, 0.019484362, -0.11434962,
154 -0.0074034138, -0.06314844, -0.092981495, 0.0062155537,
155 -0.025034338, -0.0028890965, 0.048929527, 0.06235075,
156 0.10665918, -0.032036792, -0.08505916, -0.10843358,
157 -0.13002433, -0.036816437, -0.02130134, -0.016518239,
158 0.0047691227, -0.0025825808, 0.066017866, 0.029991534,
159 -0.10652836, -0.1037554, -0.13056071, -0.03266643,
160 -0.033702414, -0.006473424, -0.04611692, 0.014419339,
161 -0.025174323, 0.0396852, 0.081777506, 0.06157468,
162 0.10210095, -0.009658194, 0.046511717, 0.03603906,
163 0.0069369148, 0.015960095, -0.06507666, 0.09551598,
164 0.053568836, 0.06408714, 0.12835667, -0.008714329,
165 -0.20211966, -0.12093674, 0.029450472, 0.2849013,
166 -0.029227901, 0.1164364, -0.08560263, 0.09941786,
167 -0.036999565, -0.028842626, -0.0033637602, -0.017012902,
168 -0.09720865, -0.11193351, -0.029155117, -0.017936034,
169 -0.009768936, -0.04223324, -0.036159635, 0.06505112,
170 -0.021742892, -0.023377212, -0.07221364, -0.06430552,
171 0.05453865, 0.091149814, 0.06387331, 0.007518393,
172 0.055960953, 0.069779344, 0.046411168, 0.10509911,
173 0.07463894, 0.0075130584, 0.012850982, 0.04555431,
174 0.056955688, 0.06555285, 0.050801456, -0.009862683,
175 0.00826772, -0.026555609, -0.0073611983, -0.0014897042,
177 input_to_output_weights: [
178 -0.0998932, -0.07201956, -0.052803773, -0.15629593, -0.15001918,
179 -0.07650751, 0.02359855, -0.075155355, -0.08037709, -0.15093534,
180 0.029517552, -0.04751393, 0.010350531, -0.02664851, -0.016839722,
181 -0.023121163, 0.0077019283, 0.012851257, -0.05040649, -0.0129761,
182 -0.021737747, -0.038305793, -0.06870586, -0.01481247, -0.001285394,
183 0.10124236, 0.083122835, 0.053313006, -0.062235646, -0.075637154,
184 -0.027833903, 0.029774971, 0.1130802, 0.09218906, 0.09506135,
185 -0.086665764, -0.037162706, -0.038880914, -0.035832845, -0.014481564,
186 -0.09825003, -0.12048569, -0.097665586, -0.05287633, -0.0964047,
187 -0.11366429, 0.035777505, 0.13568819, 0.052451383, 0.050649304,
188 0.05798951, -0.021852335, -0.099848844, 0.014740475, -0.078897946,
189 0.04974699, 0.014160473, 0.06973932, 0.04964942, 0.033364646,
190 0.08190124, 0.025535367, 0.050893165, 0.048514254, 0.06945813,
191 -0.078907564, -0.06707616, -0.11844508, -0.09986688, -0.07509403,
192 0.06263226, 0.14925587, 0.20188436, 0.12098451, 0.14639415,
193 0.0015017595, -0.014267382, -0.03417257, 0.012711468, 0.0028300495,
194 -0.024758482, -0.05098548, -0.0821182, 0.014225672, 0.021544158,
195 0.08949725, 0.07505268, -0.0020780868, 0.04908258, 0.06476295,
196 -0.022907063, 0.027562456, 0.040185735, 0.019567577, -0.015598739,
197 -0.049097303, -0.017121866, -0.083368234, -0.02332002, -0.0840956,
199 input_gate_bias: [0.02234832, 0.14757581, 0.18176508, 0.10380666,
200 0.053110216, -0.06928846, -0.13942584, -0.11816189,
201 0.19483899, 0.03652339, -0.10250295, 0.036714908,
202 -0.18426876, 0.036065217, 0.21810818, 0.02383196,
203 -0.043370757, 0.08690144, -0.04444982, 0.00030581196],
204 forget_gate_bias: [0.035185695, -0.042891346, -0.03032477, 0.23027696,
205 0.11098921, 0.15378423, 0.09263801, 0.09790885,
206 0.09508917, 0.061199076, 0.07665568, -0.015443159,
207 -0.03499149, 0.046190713, 0.08895977, 0.10899629,
208 0.40694186, 0.06030037, 0.012413437, -0.06108739],
209 cell_gate_bias: [-0.024379363, 0.0055531194, 0.23377132, 0.033463873,
210 -0.1483596, -0.10639995, -0.091433935, 0.058573797,
211 -0.06809782, -0.07889636, -0.043246906, -0.09829136,
212 -0.4279842, 0.034901652, 0.18797937, 0.0075234566,
213 0.016178843, 0.1749513, 0.13975595, 0.92058027],
214 output_gate_bias: [0.046159424, -0.0012809046, 0.03563469, 0.12648113,
215 0.027195795, 0.35373217, -0.018957434, 0.008907322,
216 -0.0762701, 0.12018895, 0.04216877, 0.0022856654,
217 0.040952638, 0.3147856, 0.08225149, -0.057416286,
218 -0.14995944, -0.008040261, 0.13208859, 0.029760877],
219 recurrent_to_input_weights: [
220 -0.001374326, -0.078856036, 0.10672688, 0.029162422,
221 -0.11585556, 0.02557986, -0.13446963, -0.035785314,
222 -0.01244275, 0.025961924, -0.02337298, -0.044228926,
223 -0.055839065, -0.046598054, -0.010546039, -0.06900766,
224 0.027239809, 0.022582639, -0.013296484, -0.05459212,
225 0.08981, -0.045407712, 0.08682226, -0.06867011,
226 -0.14390695, -0.02916037, 0.000996957, 0.091420636,
227 0.14283475, -0.07390571, -0.06402044, 0.062524505,
228 -0.093129106, 0.04860203, -0.08364217, -0.08119002,
229 0.009352075, 0.22920375, 0.0016303885, 0.11583097,
230 -0.13732095, 0.012405723, -0.07551853, 0.06343048,
231 0.12162708, -0.031923793, -0.014335606, 0.01790974,
232 -0.10650317, -0.0724401, 0.08554849, -0.05727212,
233 0.06556731, -0.042729504, -0.043227166, 0.011683251,
234 -0.013082158, -0.029302018, -0.010899579, -0.062036745,
235 -0.022509435, -0.00964907, -0.01567329, 0.04260106,
236 -0.07787477, -0.11576462, 0.017356863, 0.048673786,
237 -0.017577527, -0.05527947, -0.082487635, -0.040137455,
238 -0.10820036, -0.04666372, 0.022746278, -0.07851417,
239 0.01068115, 0.032956902, 0.022433773, 0.0026891115,
240 0.08944216, -0.0685835, 0.010513544, 0.07228705,
241 0.02032331, -0.059686817, -0.0005566496, -0.086984694,
242 0.040414046, -0.1380399, 0.094208956, -0.05722982,
243 0.012092817, -0.04989123, -0.086576, -0.003399834,
244 -0.04696032, -0.045747425, 0.10091314, 0.048676282,
245 -0.029037097, 0.031399418, -0.0040285117, 0.047237843,
246 0.09504992, 0.041799378, -0.049185462, -0.031518843,
247 -0.10516937, 0.026374253, 0.10058866, -0.0033195973,
248 -0.041975245, 0.0073591834, 0.0033782164, -0.004325073,
249 -0.10167381, 0.042500053, -0.01447153, 0.06464186,
250 -0.017142897, 0.03312627, 0.009205989, 0.024138335,
251 -0.011337001, 0.035530265, -0.010912711, 0.0706555,
252 -0.005894094, 0.051841937, -0.1401738, -0.02351249,
253 0.0365468, 0.07590991, 0.08838724, 0.021681072,
254 -0.10086113, 0.019608743, -0.06195883, 0.077335775,
255 0.023646897, -0.095322326, 0.02233014, 0.09756986,
256 -0.048691444, -0.009579111, 0.07595467, 0.11480546,
257 -0.09801813, 0.019894179, 0.08502348, 0.004032281,
258 0.037211012, 0.068537936, -0.048005626, -0.091520436,
259 -0.028379958, -0.01556313, 0.06554592, -0.045599163,
260 -0.01672207, -0.020169014, -0.011877351, -0.20212261,
261 0.010889619, 0.0047078193, 0.038385306, 0.08540671,
262 -0.017140968, -0.0035865551, 0.016678626, 0.005633034,
263 0.015963363, 0.00871737, 0.060130805, 0.028611384,
264 0.10109069, -0.015060172, -0.07894427, 0.06401885,
265 0.011584063, -0.024466386, 0.0047652307, -0.09041358,
266 0.030737216, -0.0046374933, 0.14215417, -0.11823516,
267 0.019899689, 0.006106124, -0.027092824, 0.0786356,
268 0.05052217, -0.058925, -0.011402121, -0.024987547,
269 -0.0013661642, -0.06832946, -0.015667673, -0.1083353,
270 -0.00096863037, -0.06988685, -0.053350925, -0.027275559,
271 -0.033664223, -0.07978348, -0.025200296, -0.017207067,
272 -0.058403496, -0.055697463, 0.005798788, 0.12965427,
273 -0.062582195, 0.0013350133, -0.10482091, 0.0379771,
274 0.072521195, -0.0029455067, -0.13797039, -0.03628521,
275 0.013806405, -0.017858358, -0.01008298, -0.07700066,
276 -0.017081132, 0.019358726, 0.0027079724, 0.004635139,
277 0.062634714, -0.02338735, -0.039547626, -0.02050681,
278 0.03385117, -0.083611414, 0.002862572, -0.09421313,
279 0.058618143, -0.08598433, 0.00972939, 0.023867095,
280 -0.053934585, -0.023203006, 0.07452513, -0.048767887,
281 -0.07314807, -0.056307215, -0.10433547, -0.06440842,
282 0.04328182, 0.04389765, -0.020006588, -0.09076438,
283 -0.11652589, -0.021705797, 0.03345259, -0.010329105,
284 -0.025767034, 0.013057034, -0.07316461, -0.10145612,
285 0.06358255, 0.18531723, 0.07759293, 0.12006465,
286 0.1305557, 0.058638252, -0.03393652, 0.09622831,
287 -0.16253184, -2.4580743e-06, 0.079869635, -0.070196845,
288 -0.005644518, 0.06857898, -0.12598175, -0.035084512,
289 0.03156317, -0.12794146, -0.031963028, 0.04692781,
290 0.030070418, 0.0071660685, -0.095516115, -0.004643372,
291 0.040170413, -0.062104587, -0.0037324072, 0.0554317,
292 0.08184801, -0.019164372, 0.06791302, 0.034257166,
293 -0.10307039, 0.021943003, 0.046745934, 0.0790918,
294 -0.0265588, -0.007824208, 0.042546265, -0.00977924,
295 -0.0002440307, -0.017384544, -0.017990116, 0.12252321,
296 -0.014512694, -0.08251313, 0.08861942, 0.13589665,
297 0.026351685, 0.012641483, 0.07466548, 0.044301085,
298 -0.045414884, -0.051112458, 0.03444247, -0.08502782,
299 -0.04106223, -0.028126027, 0.028473156, 0.10467447,
301 recurrent_to_cell_weights: [
302 -0.037322544, 0.018592842, 0.0056175636, -0.06253426,
303 0.055647098, -0.05713207, -0.05626563, 0.005559383,
304 0.03375411, -0.025757805, -0.088049285, 0.06017052,
305 -0.06570978, 0.007384076, 0.035123326, -0.07920549,
306 0.053676967, 0.044480428, -0.07663568, 0.0071805613,
307 0.08089997, 0.05143358, 0.038261272, 0.03339287,
308 -0.027673481, 0.044746667, 0.028349208, 0.020090483,
309 -0.019443132, -0.030755889, -0.0040000007, 0.04465846,
310 -0.021585021, 0.0031670958, 0.0053199246, -0.056117613,
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572 0.0077946745, 0.15140012, 0.29405436, 0.120285,
573 -0.188994, -0.027265169, 0.043389652, -0.022061434,
574 0.014777949, -0.20203483, 0.094781205, 0.19100232,
575 0.13987629, -0.036132768, -0.06426278, -0.05108664,
576 0.13221376, 0.009441198, -0.16715929, 0.15859416,
577 -0.040437475, 0.050779544, -0.022187516, 0.012166504,
578 0.027685808, -0.07675938, -0.0055694645, -0.09444123,
579 0.0046453946, 0.050794356, 0.10770313, -0.20790008,
580 -0.07149004, -0.11425117, 0.008225835, -0.035802525,
581 0.14374903, 0.15262283, 0.048710253, 0.1847461,
582 -0.007487823, 0.11000021, -0.09542012, 0.22619456,
583 -0.029149994, 0.08527916, 0.009043713, 0.0042746216,
584 0.016261552, 0.022461696, 0.12689082, -0.043589946,
585 -0.12035478, -0.08361797, -0.050666027, -0.1248618,
586 -0.1275799, -0.071875185, 0.07377272, 0.09944291,
587 -0.18897448, -0.1593054, -0.06526116, -0.040107165,
588 -0.004618631, -0.067624845, -0.007576253, 0.10727444,
589 0.041546922, -0.20424393, 0.06907816, 0.050412357,
590 0.00724631, 0.039827548, 0.12449835, 0.10747581,
591 0.13708383, 0.09134148, -0.12617786, -0.06428341,
592 0.09956831, 0.1208086, -0.14676677, -0.0727722,
593 0.1126304, 0.010139365, 0.015571211, -0.038128063,
594 0.022913318, -0.042050496, 0.16842307, -0.060597885,
595 0.10531834, -0.06411776, -0.07451711, -0.03410368,
596 -0.13393489, 0.06534304, 0.003620307, 0.04490757,
597 0.05970546, 0.05197996, 0.02839995, 0.10434969,
598 -0.013699693, -0.028353551, -0.07260381, 0.047201227,
599 -0.024575593, -0.036445823, 0.07155557, 0.009672501,
600 -0.02328883, 0.009533515, -0.03606021, -0.07421458,
601 -0.028082801, -0.2678904, -0.13221288, 0.18419984,
602 -0.13012612, -0.014588381, -0.035059117, -0.04824723,
603 0.07830115, -0.056184657, 0.03277091, 0.025466874,
604 0.14494097, -0.12522776, -0.098633975, -0.10766018,
605 -0.08317623, 0.08594209, 0.07749552, 0.039474737,
606 0.1776665, -0.07409566, -0.0477268, 0.29323658,
607 0.10801441, 0.1154011, 0.013952499, 0.10739139,
608 0.10708251, -0.051456142, 0.0074137426, -0.10430189,
609 0.10034707, 0.045594677, 0.0635285, -0.0715442,
610 -0.089667566, -0.10811871, 0.00026344223, 0.08298446,
611 -0.009525053, 0.006585689, -0.24567553, -0.09450807,
612 0.09648481, 0.026996298, -0.06419476, -0.04752702,
613 -0.11063944, -0.23441927, -0.17608605, -0.052156363,
614 0.067035615, 0.19271925, -0.0032889997, -0.043264326,
615 0.09663576, -0.057112187, -0.10100678, 0.0628376,
616 0.04447668, 0.017961001, -0.10094388, -0.10190601,
617 0.18335468, 0.10494553, -0.052095775, -0.0026118709,
618 0.10539724, -0.04383912, -0.042349473, 0.08438151,
619 -0.1947263, 0.02251204, 0.11216432, -0.10307853,
620 0.17351969, -0.039091777, 0.08066188, -0.00561982,
621 0.12633002, 0.11335965, -0.0088127935, -0.019777594,
622 0.06864014, -0.059751723, 0.016233567, -0.06894641,
623 -0.28651384, -0.004228674, 0.019708522, -0.16305895,
624 -0.07468996, -0.0855457, 0.099339016, -0.07580735,
625 -0.13775392, 0.08434318, 0.08330512, -0.12131499,
626 0.031935584, 0.09180414, -0.08876437, -0.08049874,
627 0.008753825, 0.03498998, 0.030215185, 0.03907079,
628 0.089751154, 0.029194152, -0.03337423, -0.019092513,
629 0.04331237, 0.04299654, -0.036394123, -0.12915532,
630 0.09793732, 0.07512415, -0.11319543, -0.032502122,
631 0.15661901, 0.07671967, -0.005491124, -0.19379048,
632 -0.218606, 0.21448623, 0.017840758, 0.1416943,
633 -0.07051762, 0.19488361, 0.02664691, -0.18104725,
634 -0.09334311, 0.15026465, -0.15493552, -0.057762887,
635 -0.11604192, -0.262013, -0.01391798, 0.012185008,
636 0.11156489, -0.07483202, 0.06693364, -0.26151478,
637 0.046425626, 0.036540434, -0.16435726, 0.17338543,
638 -0.21401681, -0.11385144, -0.08283257, -0.069031075,
639 0.030635102, 0.010969227, 0.11109743, 0.010919218,
640 0.027526086, 0.13519906, 0.01891392, -0.046839405,
641 -0.040167913, 0.017953383, -0.09700955, 0.0061885654,
642 -0.07000971, 0.026893595, -0.038844477, 0.14543656,
644 projection_bias: [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8,
645 0.9, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6],
647 input_layer_norm_weights: [],
648 forget_layer_norm_weights: [],
649 cell_layer_norm_weights: [],
650 output_layer_norm_weights: []
655 0.787926, 0.151646, 0.071352, 0.118426, 0.458058, # step 0
656 0.596268, 0.998386, 0.568695, 0.864524, 0.571277, # step 1
657 0.073204, 0.296072, 0.743333, 0.069199, 0.045348, # step 2
658 0.867394, 0.291279, 0.013714, 0.482521, 0.626339, # step 3
660 0.295743, 0.544053, 0.690064, 0.858138, 0.497181, # step 0
661 0.642421, 0.524260, 0.134799, 0.003639, 0.162482, # step 1
662 0.640394, 0.930399, 0.050782, 0.432485, 0.988078, # step 2
663 0.082922, 0.563329, 0.865614, 0.333232, 0.259916, # step 3
668 0.0960319489, 0.229351997, 0.297207743, 0.415997744, 0.491644233,
669 0.578822136, 0.728351235, 0.788540304, 0.909073055, 0.975599587,
670 1.08478093, 1.17409372, 1.30914319, 1.4041512, 1.51714694,
671 1.61342025, 0.0634541437, 0.190279216, 0.317923307, 0.415168911,
672 0.458113253, 0.609743774, 0.731511116, 0.795806408, 0.876155913,
673 0.960330188, 1.12396312, 1.22149014, 1.33917773, 1.43213499,
674 1.54139447, 1.65451813, 0.0485293195, 0.160991609, 0.337073475,
675 0.428976893, 0.459505379, 0.617044866, 0.743735075, 0.790821671,
676 0.85271728, 0.946818829, 1.12779701, 1.23345077, 1.35309088,
677 1.44595909, 1.56173062, 1.67839324, 0.0445971154, 0.156434938,
678 0.341761589, 0.425259203, 0.449760497, 0.633765697, 0.745093822,
679 0.791106999, 0.84820503, 0.952787101, 1.13438797, 1.24063754,
680 1.34668994, 1.44879568, 1.57038593, 1.67956686,
682 0.0861309841, 0.228726774, 0.296653062, 0.40733397, 0.47120741,
683 0.581307411, 0.719366193, 0.788456261, 0.904226124, 0.965476751,
684 1.10223258, 1.19042683, 1.32106233, 1.41333091, 1.51509535,
685 1.62168002, 0.0652779415, 0.18218407, 0.324066937, 0.42611438,
686 0.47292757, 0.602282405, 0.739310443, 0.791508496, 0.870626807,
687 0.955534995, 1.10976851, 1.21598971, 1.34197009, 1.43256509,
688 1.54804492, 1.65581059, 0.0492607877, 0.169714347, 0.332315415,
689 0.419173867, 0.44699502, 0.630063772, 0.737177074, 0.792844594,
690 0.858417571, 0.956391335, 1.13453305, 1.23976779, 1.34693861,
691 1.4410423, 1.55988359, 1.67204297, 0.0390465111, 0.15099439,
692 0.3439475, 0.424439192, 0.444207728, 0.632501483, 0.742233515,
693 0.791400731, 0.845713973, 0.944575012, 1.14116096, 1.24791968,
694 1.35954499, 1.45086145, 1.56633317, 1.68943977,
698 output: golden_output,
701 input0[input] = test_input
702 input0[output_state_in] = [0 for _ in range(n_batch * n_output)]
703 input0[cell_state_in] = [0 for _ in range(n_batch * n_cell)]
705 Example((input0, output0))
projects / platform / core / ml / nnfw.git / blob