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|
#
# Copyright (C) 2017 The Android Open Source Project
#
# 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.
#
# LSTM Test, With Peephole, With Projection, No Clipping
model = Model()
n_batch = 2
n_input = 5
# n_cell and n_output have the same size when there is no projection.
n_cell = 20
n_output = 16
input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_input))
input_to_input_weights = Input("input_to_input_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input))
input_to_forget_weights = Input("input_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input))
input_to_cell_weights = Input("input_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input))
input_to_output_weights = Input("input_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input))
recurrent_to_input_weights = Input("recurrent_to_intput_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
recurrent_to_forget_weights = Input("recurrent_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
recurrent_to_cell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
recurrent_to_output_weights = Input("recurrent_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
cell_to_input_weights = Input("cell_to_input_weights", "TENSOR_FLOAT32", "{%d}" % (n_cell))
cell_to_forget_weights = Input("cell_to_forget_weights", "TENSOR_FLOAT32", "{%d}" %(n_cell))
cell_to_output_weights = Input("cell_to_output_weights", "TENSOR_FLOAT32", "{%d}" % (n_cell))
input_gate_bias = Input("input_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell))
forget_gate_bias = Input("forget_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell))
cell_gate_bias = Input("cell_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell))
output_gate_bias = Input("output_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell))
projection_weights = Input("projection_weights", "TENSOR_FLOAT32", "{%d,%d}" % (n_output, n_cell))
projection_bias = Input("projection_bias", "TENSOR_FLOAT32", "{0}")
output_state_in = Input("output_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
cell_state_in = Input("cell_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell))
activation_param = Int32Scalar("activation_param", 4) # Tanh
cell_clip_param = Float32Scalar("cell_clip_param", 0.)
proj_clip_param = Float32Scalar("proj_clip_param", 0.)
scratch_buffer = IgnoredOutput("scratch_buffer", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, (n_cell * 4)))
output_state_out = Output("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
cell_state_out = Output("cell_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell))
output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
# TODO: need support for more than one output
model = model.Operation("LSTM",
input,
input_to_input_weights,
input_to_forget_weights,
input_to_cell_weights,
input_to_output_weights,
recurrent_to_input_weights,
recurrent_to_forget_weights,
recurrent_to_cell_weights,
recurrent_to_output_weights,
cell_to_input_weights,
cell_to_forget_weights,
cell_to_output_weights,
input_gate_bias,
forget_gate_bias,
cell_gate_bias,
output_gate_bias,
projection_weights,
projection_bias,
output_state_in,
cell_state_in,
activation_param,
cell_clip_param,
proj_clip_param
).To([scratch_buffer, output_state_out, cell_state_out, output])
input0 = {input_to_input_weights: [
0.021393683, 0.06124551, 0.046905167, -0.014657677, -0.03149463,
0.09171803, 0.14647801, 0.10797193, -0.0057968358, 0.0019193048,
-0.2726754, 0.10154029, -0.018539885, 0.080349885, -0.10262385,
-0.022599787, -0.09121155, -0.008675967, -0.045206103, -0.0821282,
-0.008045952, 0.015478081, 0.055217247, 0.038719587, 0.044153627,
-0.06453243, 0.05031825, -0.046935108, -0.008164439, 0.014574226,
-0.1671009, -0.15519552, -0.16819797, -0.13971269, -0.11953059,
0.25005487, -0.22790983, 0.009855087, -0.028140958, -0.11200698,
0.11295408, -0.0035217577, 0.054485075, 0.05184695, 0.064711206,
0.10989193, 0.11674786, 0.03490607, 0.07727357, 0.11390585,
-0.1863375, -0.1034451, -0.13945189, -0.049401227, -0.18767063,
0.042483903, 0.14233552, 0.13832581, 0.18350165, 0.14545603,
-0.028545704, 0.024939531, 0.050929718, 0.0076203286, -0.0029723682,
-0.042484224, -0.11827596, -0.09171104, -0.10808628, -0.16327988,
-0.2273378, -0.0993647, -0.017155107, 0.0023917493, 0.049272764,
0.0038534778, 0.054764505, 0.089753784, 0.06947234, 0.08014476,
-0.04544234, -0.0497073, -0.07135631, -0.048929106, -0.004042012,
-0.009284026, 0.018042054, 0.0036860977, -0.07427302, -0.11434604,
-0.018995456, 0.031487543, 0.012834908, 0.019977754, 0.044256654,
-0.39292613, -0.18519334, -0.11651281, -0.06809892, 0.011373677],
input_to_forget_weights: [
-0.0018401089, -0.004852237, 0.03698424, 0.014181704, 0.028273236,
-0.016726194, -0.05249759, -0.10204261, 0.00861066, -0.040979505,
-0.009899187, 0.01923892, -0.028177269, -0.08535103, -0.14585495,
0.10662567, -0.01909731, -0.017883534, -0.0047269356, -0.045103323,
0.0030784295, 0.076784775, 0.07463696, 0.094531395, 0.0814421,
-0.12257899, -0.033945758, -0.031303465, 0.045630626, 0.06843887,
-0.13492945, -0.012480007, -0.0811829, -0.07224499, -0.09628791,
0.045100946, 0.0012300825, 0.013964662, 0.099372394, 0.02543059,
0.06958324, 0.034257296, 0.0482646, 0.06267997, 0.052625068,
0.12784666, 0.07077897, 0.025725935, 0.04165009, 0.07241905,
0.018668644, -0.037377294, -0.06277783, -0.08833636, -0.040120605,
-0.011405586, -0.007808335, -0.010301386, -0.005102167, 0.027717464,
0.05483423, 0.11449111, 0.11289652, 0.10939839, 0.13396506,
-0.08402166, -0.01901462, -0.044678304, -0.07720565, 0.014350063,
-0.11757958, -0.0652038, -0.08185733, -0.076754324, -0.092614375,
0.10405491, 0.052960336, 0.035755895, 0.035839386, -0.012540553,
0.036881298, 0.02913376, 0.03420159, 0.05448447, -0.054523353,
0.02582715, 0.02327355, -0.011857179, -0.0011980024, -0.034641717,
-0.026125094, -0.17582615, -0.15923657, -0.27486774, -0.0006143371,
0.0001771948, -8.470171e-05, 0.02651807, 0.045790765, 0.06956496],
input_to_cell_weights: [
-0.04580283, -0.09549462, -0.032418985, -0.06454633,
-0.043528453, 0.043018587, -0.049152344, -0.12418144,
-0.078985475, -0.07596889, 0.019484362, -0.11434962,
-0.0074034138, -0.06314844, -0.092981495, 0.0062155537,
-0.025034338, -0.0028890965, 0.048929527, 0.06235075,
0.10665918, -0.032036792, -0.08505916, -0.10843358,
-0.13002433, -0.036816437, -0.02130134, -0.016518239,
0.0047691227, -0.0025825808, 0.066017866, 0.029991534,
-0.10652836, -0.1037554, -0.13056071, -0.03266643,
-0.033702414, -0.006473424, -0.04611692, 0.014419339,
-0.025174323, 0.0396852, 0.081777506, 0.06157468,
0.10210095, -0.009658194, 0.046511717, 0.03603906,
0.0069369148, 0.015960095, -0.06507666, 0.09551598,
0.053568836, 0.06408714, 0.12835667, -0.008714329,
-0.20211966, -0.12093674, 0.029450472, 0.2849013,
-0.029227901, 0.1164364, -0.08560263, 0.09941786,
-0.036999565, -0.028842626, -0.0033637602, -0.017012902,
-0.09720865, -0.11193351, -0.029155117, -0.017936034,
-0.009768936, -0.04223324, -0.036159635, 0.06505112,
-0.021742892, -0.023377212, -0.07221364, -0.06430552,
0.05453865, 0.091149814, 0.06387331, 0.007518393,
0.055960953, 0.069779344, 0.046411168, 0.10509911,
0.07463894, 0.0075130584, 0.012850982, 0.04555431,
0.056955688, 0.06555285, 0.050801456, -0.009862683,
0.00826772, -0.026555609, -0.0073611983, -0.0014897042],
input_to_output_weights: [
-0.0998932, -0.07201956, -0.052803773, -0.15629593, -0.15001918,
-0.07650751, 0.02359855, -0.075155355, -0.08037709, -0.15093534,
0.029517552, -0.04751393, 0.010350531, -0.02664851, -0.016839722,
-0.023121163, 0.0077019283, 0.012851257, -0.05040649, -0.0129761,
-0.021737747, -0.038305793, -0.06870586, -0.01481247, -0.001285394,
0.10124236, 0.083122835, 0.053313006, -0.062235646, -0.075637154,
-0.027833903, 0.029774971, 0.1130802, 0.09218906, 0.09506135,
-0.086665764, -0.037162706, -0.038880914, -0.035832845, -0.014481564,
-0.09825003, -0.12048569, -0.097665586, -0.05287633, -0.0964047,
-0.11366429, 0.035777505, 0.13568819, 0.052451383, 0.050649304,
0.05798951, -0.021852335, -0.099848844, 0.014740475, -0.078897946,
0.04974699, 0.014160473, 0.06973932, 0.04964942, 0.033364646,
0.08190124, 0.025535367, 0.050893165, 0.048514254, 0.06945813,
-0.078907564, -0.06707616, -0.11844508, -0.09986688, -0.07509403,
0.06263226, 0.14925587, 0.20188436, 0.12098451, 0.14639415,
0.0015017595, -0.014267382, -0.03417257, 0.012711468, 0.0028300495,
-0.024758482, -0.05098548, -0.0821182, 0.014225672, 0.021544158,
0.08949725, 0.07505268, -0.0020780868, 0.04908258, 0.06476295,
-0.022907063, 0.027562456, 0.040185735, 0.019567577, -0.015598739,
-0.049097303, -0.017121866, -0.083368234, -0.02332002, -0.0840956],
input_gate_bias: [
0.02234832, 0.14757581, 0.18176508, 0.10380666, 0.053110216,
-0.06928846, -0.13942584, -0.11816189, 0.19483899, 0.03652339,
-0.10250295, 0.036714908, -0.18426876, 0.036065217, 0.21810818,
0.02383196, -0.043370757, 0.08690144, -0.04444982, 0.00030581196],
forget_gate_bias: [
0.035185695, -0.042891346, -0.03032477, 0.23027696,
0.11098921, 0.15378423, 0.09263801, 0.09790885,
0.09508917, 0.061199076, 0.07665568, -0.015443159,
-0.03499149, 0.046190713, 0.08895977, 0.10899629,
0.40694186, 0.06030037, 0.012413437, -0.06108739],
cell_gate_bias: [
-0.024379363, 0.0055531194, 0.23377132, 0.033463873,
-0.1483596, -0.10639995, -0.091433935, 0.058573797,
-0.06809782, -0.07889636, -0.043246906, -0.09829136,
-0.4279842, 0.034901652, 0.18797937, 0.0075234566,
0.016178843, 0.1749513, 0.13975595, 0.92058027],
output_gate_bias: [
0.046159424, -0.0012809046, 0.03563469, 0.12648113, 0.027195795,
0.35373217, -0.018957434, 0.008907322, -0.0762701, 0.12018895,
0.04216877, 0.0022856654, 0.040952638, 0.3147856, 0.08225149,
-0.057416286, -0.14995944, -0.008040261, 0.13208859, 0.029760877],
recurrent_to_input_weights: [
-0.001374326, -0.078856036, 0.10672688, 0.029162422,
-0.11585556, 0.02557986, -0.13446963, -0.035785314,
-0.01244275, 0.025961924, -0.02337298, -0.044228926,
-0.055839065, -0.046598054, -0.010546039, -0.06900766,
0.027239809, 0.022582639, -0.013296484, -0.05459212,
0.08981, -0.045407712, 0.08682226, -0.06867011,
-0.14390695, -0.02916037, 0.000996957, 0.091420636,
0.14283475, -0.07390571, -0.06402044, 0.062524505,
-0.093129106, 0.04860203, -0.08364217, -0.08119002,
0.009352075, 0.22920375, 0.0016303885, 0.11583097,
-0.13732095, 0.012405723, -0.07551853, 0.06343048,
0.12162708, -0.031923793, -0.014335606, 0.01790974,
-0.10650317, -0.0724401, 0.08554849, -0.05727212,
0.06556731, -0.042729504, -0.043227166, 0.011683251,
-0.013082158, -0.029302018, -0.010899579, -0.062036745,
-0.022509435, -0.00964907, -0.01567329, 0.04260106,
-0.07787477, -0.11576462, 0.017356863, 0.048673786,
-0.017577527, -0.05527947, -0.082487635, -0.040137455,
-0.10820036, -0.04666372, 0.022746278, -0.07851417,
0.01068115, 0.032956902, 0.022433773, 0.0026891115,
0.08944216, -0.0685835, 0.010513544, 0.07228705,
0.02032331, -0.059686817, -0.0005566496, -0.086984694,
0.040414046, -0.1380399, 0.094208956, -0.05722982,
0.012092817, -0.04989123, -0.086576, -0.003399834,
-0.04696032, -0.045747425, 0.10091314, 0.048676282,
-0.029037097, 0.031399418, -0.0040285117, 0.047237843,
0.09504992, 0.041799378, -0.049185462, -0.031518843,
-0.10516937, 0.026374253, 0.10058866, -0.0033195973,
-0.041975245, 0.0073591834, 0.0033782164, -0.004325073,
-0.10167381, 0.042500053, -0.01447153, 0.06464186,
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-0.0190714, -0.0869359, 0.037901703, 0.0610107, 0.07202949,
0.01675338, 0.086139716, -0.08795751, -0.014898893, -0.023771819,
-0.01965048, 0.007955471, -0.043740474, 0.03346837, -0.10549954,
0.090567775, 0.042013682, -0.03176985, 0.12569028, -0.02421228,
-0.029526481, 0.023851605, 0.031539805, 0.05292009, -0.02344001,
-0.07811758, -0.08834428, 0.10094801, 0.16594367, -0.06861939,
-0.021256343, -0.041093912, -0.06669611, 0.035498552, 0.021757556,
-0.09302526, -0.015403468, -0.06614931, -0.051798206, -0.013874718,
0.03630673, 0.010412845, -0.08077351, 0.046185967, 0.0035662893,
0.03541868, -0.094149634, -0.034814864, 0.003128424, -0.020674974,
-0.03944324, -0.008110165, -0.11113267, 0.08484226, 0.043586485,
0.040582247, 0.0968012, -0.065249965, -0.028036479, 0.0050708856,
0.0017462453, 0.0326779, 0.041296225, 0.09164146, -0.047743853,
-0.015952192, -0.034451712, 0.084197424, -0.05347844, -0.11768019,
0.085926116, -0.08251791, -0.045081906, 0.0948852, 0.068401024,
0.024856757, 0.06978981, -0.057309967, -0.012775832, -0.0032452994,
0.01977615, -0.041040014, -0.024264973, 0.063464895, 0.05431621],
cell_to_input_weights: [
0.040369894, 0.030746894, 0.24704495, 0.018586371, -0.037586458,
-0.15312155, -0.11812848, -0.11465643, 0.20259799, 0.11418174,
-0.10116027, -0.011334949, 0.12411352, -0.076769054, -0.052169047,
0.21198851, -0.38871562, -0.09061183, -0.09683246, -0.21929175],
cell_to_forget_weights: [
-0.01998659, -0.15568835, -0.24248174, -0.012770197, 0.041331276,
-0.072311886, -0.052123554, -0.0066330447, -0.043891653, 0.036225766,
-0.047248036, 0.021479502, 0.033189066, 0.11952997, -0.020432774,
0.64658105, -0.06650122, -0.03467612, 0.095340036, 0.23647355],
cell_to_output_weights: [
0.08286371, -0.08261836, -0.51210177, 0.002913762, 0.17764764,
-0.5495371, -0.08460716, -0.24552552, 0.030037103, 0.04123544,
-0.11940523, 0.007358328, 0.1890978, 0.4833202, -0.34441817,
0.36312827, -0.26375428, 0.1457655, -0.19724406, 0.15548733],
projection_weights: [
-0.009802181, 0.09401916, 0.0717386, -0.13895074, 0.09641832,
0.060420845, 0.08539281, 0.054285463, 0.061395317, 0.034448683,
-0.042991187, 0.019801661, -0.16840284, -0.015726732, -0.23041931,
-0.024478018, -0.10959692, -0.013875541, 0.18600968, -0.061274476,
0.0138165, -0.08160894, -0.07661644, 0.032372914, 0.16169067,
0.22465782, -0.03993472, -0.004017731, 0.08633481, -0.28869787,
0.08682067, 0.17240396, 0.014975425, 0.056431185, 0.031037588,
0.16702051, 0.0077946745, 0.15140012, 0.29405436, 0.120285,
-0.188994, -0.027265169, 0.043389652, -0.022061434, 0.014777949,
-0.20203483, 0.094781205, 0.19100232, 0.13987629, -0.036132768,
-0.06426278, -0.05108664, 0.13221376, 0.009441198, -0.16715929,
0.15859416, -0.040437475, 0.050779544, -0.022187516, 0.012166504,
0.027685808, -0.07675938, -0.0055694645, -0.09444123, 0.0046453946,
0.050794356, 0.10770313, -0.20790008, -0.07149004, -0.11425117,
0.008225835, -0.035802525, 0.14374903, 0.15262283, 0.048710253,
0.1847461, -0.007487823, 0.11000021, -0.09542012, 0.22619456,
-0.029149994, 0.08527916, 0.009043713, 0.0042746216, 0.016261552,
0.022461696, 0.12689082, -0.043589946, -0.12035478, -0.08361797,
-0.050666027, -0.1248618, -0.1275799, -0.071875185, 0.07377272,
0.09944291, -0.18897448, -0.1593054, -0.06526116, -0.040107165,
-0.004618631, -0.067624845, -0.007576253, 0.10727444, 0.041546922,
-0.20424393, 0.06907816, 0.050412357, 0.00724631, 0.039827548,
0.12449835, 0.10747581, 0.13708383, 0.09134148, -0.12617786,
-0.06428341, 0.09956831, 0.1208086, -0.14676677, -0.0727722,
0.1126304, 0.010139365, 0.015571211, -0.038128063, 0.022913318,
-0.042050496, 0.16842307, -0.060597885, 0.10531834, -0.06411776,
-0.07451711, -0.03410368, -0.13393489, 0.06534304, 0.003620307,
0.04490757, 0.05970546, 0.05197996, 0.02839995, 0.10434969,
-0.013699693, -0.028353551, -0.07260381, 0.047201227, -0.024575593,
-0.036445823, 0.07155557, 0.009672501, -0.02328883, 0.009533515,
-0.03606021, -0.07421458, -0.028082801, -0.2678904, -0.13221288,
0.18419984, -0.13012612, -0.014588381, -0.035059117, -0.04824723,
0.07830115, -0.056184657, 0.03277091, 0.025466874, 0.14494097,
-0.12522776, -0.098633975, -0.10766018, -0.08317623, 0.08594209,
0.07749552, 0.039474737, 0.1776665, -0.07409566, -0.0477268,
0.29323658, 0.10801441, 0.1154011, 0.013952499, 0.10739139,
0.10708251, -0.051456142, 0.0074137426, -0.10430189, 0.10034707,
0.045594677, 0.0635285, -0.0715442, -0.089667566, -0.10811871,
0.00026344223, 0.08298446, -0.009525053, 0.006585689, -0.24567553,
-0.09450807, 0.09648481, 0.026996298, -0.06419476, -0.04752702,
-0.11063944, -0.23441927, -0.17608605, -0.052156363, 0.067035615,
0.19271925, -0.0032889997, -0.043264326, 0.09663576, -0.057112187,
-0.10100678, 0.0628376, 0.04447668, 0.017961001, -0.10094388,
-0.10190601, 0.18335468, 0.10494553, -0.052095775, -0.0026118709,
0.10539724, -0.04383912, -0.042349473, 0.08438151, -0.1947263,
0.02251204, 0.11216432, -0.10307853, 0.17351969, -0.039091777,
0.08066188, -0.00561982, 0.12633002, 0.11335965, -0.0088127935,
-0.019777594, 0.06864014, -0.059751723, 0.016233567, -0.06894641,
-0.28651384, -0.004228674, 0.019708522, -0.16305895, -0.07468996,
-0.0855457, 0.099339016, -0.07580735, -0.13775392, 0.08434318,
0.08330512, -0.12131499, 0.031935584, 0.09180414, -0.08876437,
-0.08049874, 0.008753825, 0.03498998, 0.030215185, 0.03907079,
0.089751154, 0.029194152, -0.03337423, -0.019092513, 0.04331237,
0.04299654, -0.036394123, -0.12915532, 0.09793732, 0.07512415,
-0.11319543, -0.032502122, 0.15661901, 0.07671967, -0.005491124,
-0.19379048, -0.218606, 0.21448623, 0.017840758, 0.1416943,
-0.07051762, 0.19488361, 0.02664691, -0.18104725, -0.09334311,
0.15026465, -0.15493552, -0.057762887, -0.11604192, -0.262013,
-0.01391798, 0.012185008, 0.11156489, -0.07483202, 0.06693364,
-0.26151478, 0.046425626, 0.036540434, -0.16435726, 0.17338543,
-0.21401681, -0.11385144, -0.08283257, -0.069031075, 0.030635102,
0.010969227, 0.11109743, 0.010919218, 0.027526086, 0.13519906,
0.01891392, -0.046839405, -0.040167913, 0.017953383, -0.09700955,
0.0061885654, -0.07000971, 0.026893595, -0.038844477, 0.14543656],
projection_bias: [],
}
# Batch0: 4 (input_sequence_size) * 5 (n_input)
input0[input] = [0.787926, 0.151646, 0.071352, 0.118426, 0.458058]
# Batch1: 4 (input_sequence_size) * 5 (n_input)
input0[input].extend(
[0.295743, 0.544053, 0.690064, 0.858138, 0.497181],
)
input0[cell_state_in] = [ 0 for _ in range(n_batch * n_cell) ]
input0[output_state_in] = [ 0 for _ in range(n_batch * n_output) ]
output0 = {
scratch_buffer: [ 0 for x in range(n_batch * n_cell * 4) ],
cell_state_out: [
-0.0531632, -0.0118138, 0.0870833, 0.0347929,
-0.076144, -0.0659219, -0.0463811, 0.0141307,
-0.0127706, -0.03782, -0.00402401, -0.00571876,
-0.187957, -0.0247127, 0.0711425, 0.008244,
0.0492649, 0.126972, 0.0933097, 0.29848,
-0.0966178, -0.114417, 0.0387229, 0.0453255,
-0.181286, -0.0651251, -0.0996879, -0.00276995,
0.0617558, -0.0100728, 0.056304, -0.077416,
-0.162858, -0.0541251, 0.0571202, -0.0525331,
0.0724297, 0.171029, 0.141738, 0.295483,
],
output_state_out: [
-0.00396806, 0.029352, -0.00279226, 0.0159977,
-0.00835577, -0.0211779, 0.0283512, -0.0114597,
0.00907307, -0.0244004, -0.0152191, -0.0259063,
0.00914318, 0.00415119, 0.017147, 0.0134203,
-0.013869, 0.0287268, -0.00334694, 0.00733397,
-0.0287926, -0.0186926, 0.0193662, -0.0115437,
0.00422612, -0.0345232, 0.00223253, -0.00957321,
0.0210624, 0.013331, 0.0150954, 0.0216801
],
}
# Batch0: 4 (input_sequence_size) * 16 (n_output)
output0[output] = [
-0.00396806, 0.029352, -0.00279226, 0.0159977, -0.00835576,
-0.0211779, 0.0283512, -0.0114597, 0.00907307, -0.0244004,
-0.0152191, -0.0259063, 0.00914318, 0.00415118, 0.017147,
0.0134203]
# Batch1: 4 (input_sequence_size) * 16 (n_output)
output0[output].extend(
[-0.013869, 0.0287268, -0.00334693, 0.00733398, -0.0287926,
-0.0186926, 0.0193662, -0.0115437, 0.00422612, -0.0345232,
0.00223253, -0.00957321, 0.0210624, 0.013331, 0.0150954,
0.02168],
)
Example((input0, output0))
|
