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# LSTM Test, With Peephole, With Projection, No Clipping 18 19model = Model() 20 21n_batch = 2 22n_input = 5 23# n_cell and n_output have the same size when there is no projection. 24n_cell = 20 25n_output = 16 26 27input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_input)) 28 29input_to_input_weights = Input("input_to_input_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input)) 30input_to_forget_weights = Input("input_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input)) 31input_to_cell_weights = Input("input_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input)) 32input_to_output_weights = Input("input_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input)) 33 34recurrent_to_input_weights = Input("recurrent_to_intput_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output)) 35recurrent_to_forget_weights = Input("recurrent_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output)) 36recurrent_to_cell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output)) 37recurrent_to_output_weights = Input("recurrent_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output)) 38 39cell_to_input_weights = Input("cell_to_input_weights", "TENSOR_FLOAT32", "{%d}" % (n_cell)) 40cell_to_forget_weights = Input("cell_to_forget_weights", "TENSOR_FLOAT32", "{%d}" %(n_cell)) 41cell_to_output_weights = Input("cell_to_output_weights", "TENSOR_FLOAT32", "{%d}" % (n_cell)) 42 43input_gate_bias = Input("input_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell)) 44forget_gate_bias = Input("forget_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell)) 45cell_gate_bias = Input("cell_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell)) 46output_gate_bias = Input("output_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell)) 47 48projection_weights = Input("projection_weights", "TENSOR_FLOAT32", "{%d,%d}" % (n_output, n_cell)) 49projection_bias = Input("projection_bias", "TENSOR_FLOAT32", "{0}") 50 51output_state_in = Input("output_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output)) 52cell_state_in = Input("cell_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell)) 53 54activation_param = Int32Scalar("activation_param", 4) # Tanh 55cell_clip_param = Float32Scalar("cell_clip_param", 0.) 56proj_clip_param = Float32Scalar("proj_clip_param", 0.) 57 58scratch_buffer = IgnoredOutput("scratch_buffer", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, (n_cell * 4))) 59output_state_out = Output("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output)) 60cell_state_out = Output("cell_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell)) 61output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output)) 62 63model = model.Operation("LSTM", 64 input, 65 66 input_to_input_weights, 67 input_to_forget_weights, 68 input_to_cell_weights, 69 input_to_output_weights, 70 71 recurrent_to_input_weights, 72 recurrent_to_forget_weights, 73 recurrent_to_cell_weights, 74 recurrent_to_output_weights, 75 76 cell_to_input_weights, 77 cell_to_forget_weights, 78 cell_to_output_weights, 79 80 input_gate_bias, 81 forget_gate_bias, 82 cell_gate_bias, 83 output_gate_bias, 84 85 projection_weights, 86 projection_bias, 87 88 output_state_in, 89 cell_state_in, 90 91 activation_param, 92 cell_clip_param, 93 proj_clip_param 94).To([scratch_buffer, output_state_out, cell_state_out, output]) 95 96input0 = {input_to_input_weights: [ 97 0.021393683, 0.06124551, 0.046905167, -0.014657677, -0.03149463, 98 0.09171803, 0.14647801, 0.10797193, -0.0057968358, 0.0019193048, 99 -0.2726754, 0.10154029, -0.018539885, 0.080349885, -0.10262385, 100 -0.022599787, -0.09121155, -0.008675967, -0.045206103, -0.0821282, 101 -0.008045952, 0.015478081, 0.055217247, 0.038719587, 0.044153627, 102 -0.06453243, 0.05031825, -0.046935108, -0.008164439, 0.014574226, 103 -0.1671009, -0.15519552, -0.16819797, -0.13971269, -0.11953059, 104 0.25005487, -0.22790983, 0.009855087, -0.028140958, -0.11200698, 105 0.11295408, -0.0035217577, 0.054485075, 0.05184695, 0.064711206, 106 0.10989193, 0.11674786, 0.03490607, 0.07727357, 0.11390585, 107 -0.1863375, -0.1034451, -0.13945189, -0.049401227, -0.18767063, 108 0.042483903, 0.14233552, 0.13832581, 0.18350165, 0.14545603, 109 -0.028545704, 0.024939531, 0.050929718, 0.0076203286, -0.0029723682, 110 -0.042484224, -0.11827596, -0.09171104, -0.10808628, -0.16327988, 111 -0.2273378, -0.0993647, -0.017155107, 0.0023917493, 0.049272764, 112 0.0038534778, 0.054764505, 0.089753784, 0.06947234, 0.08014476, 113 -0.04544234, -0.0497073, -0.07135631, -0.048929106, -0.004042012, 114 -0.009284026, 0.018042054, 0.0036860977, -0.07427302, -0.11434604, 115 -0.018995456, 0.031487543, 0.012834908, 0.019977754, 0.044256654, 116 -0.39292613, -0.18519334, -0.11651281, -0.06809892, 0.011373677], 117 118 input_to_forget_weights: [ 119 -0.0018401089, -0.004852237, 0.03698424, 0.014181704, 0.028273236, 120 -0.016726194, -0.05249759, -0.10204261, 0.00861066, -0.040979505, 121 -0.009899187, 0.01923892, -0.028177269, -0.08535103, -0.14585495, 122 0.10662567, -0.01909731, -0.017883534, -0.0047269356, -0.045103323, 123 0.0030784295, 0.076784775, 0.07463696, 0.094531395, 0.0814421, 124 -0.12257899, -0.033945758, -0.031303465, 0.045630626, 0.06843887, 125 -0.13492945, -0.012480007, -0.0811829, -0.07224499, -0.09628791, 126 0.045100946, 0.0012300825, 0.013964662, 0.099372394, 0.02543059, 127 0.06958324, 0.034257296, 0.0482646, 0.06267997, 0.052625068, 128 0.12784666, 0.07077897, 0.025725935, 0.04165009, 0.07241905, 129 0.018668644, -0.037377294, -0.06277783, -0.08833636, -0.040120605, 130 -0.011405586, -0.007808335, -0.010301386, -0.005102167, 0.027717464, 131 0.05483423, 0.11449111, 0.11289652, 0.10939839, 0.13396506, 132 -0.08402166, -0.01901462, -0.044678304, -0.07720565, 0.014350063, 133 -0.11757958, -0.0652038, -0.08185733, -0.076754324, -0.092614375, 134 0.10405491, 0.052960336, 0.035755895, 0.035839386, -0.012540553, 135 0.036881298, 0.02913376, 0.03420159, 0.05448447, -0.054523353, 136 0.02582715, 0.02327355, -0.011857179, -0.0011980024, -0.034641717, 137 -0.026125094, -0.17582615, -0.15923657, -0.27486774, -0.0006143371, 138 0.0001771948, -8.470171e-05, 0.02651807, 0.045790765, 0.06956496], 139 140 input_to_cell_weights: [ 141 -0.04580283, -0.09549462, -0.032418985, -0.06454633, 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0.036065217, 0.21810818, 193 0.02383196, -0.043370757, 0.08690144, -0.04444982, 0.00030581196], 194 195 forget_gate_bias: [ 196 0.035185695, -0.042891346, -0.03032477, 0.23027696, 197 0.11098921, 0.15378423, 0.09263801, 0.09790885, 198 0.09508917, 0.061199076, 0.07665568, -0.015443159, 199 -0.03499149, 0.046190713, 0.08895977, 0.10899629, 200 0.40694186, 0.06030037, 0.012413437, -0.06108739], 201 202 cell_gate_bias: [ 203 -0.024379363, 0.0055531194, 0.23377132, 0.033463873, 204 -0.1483596, -0.10639995, -0.091433935, 0.058573797, 205 -0.06809782, -0.07889636, -0.043246906, -0.09829136, 206 -0.4279842, 0.034901652, 0.18797937, 0.0075234566, 207 0.016178843, 0.1749513, 0.13975595, 0.92058027], 208 209 output_gate_bias: [ 210 0.046159424, -0.0012809046, 0.03563469, 0.12648113, 0.027195795, 211 0.35373217, -0.018957434, 0.008907322, -0.0762701, 0.12018895, 212 0.04216877, 0.0022856654, 0.040952638, 0.3147856, 0.08225149, 213 -0.057416286, -0.14995944, -0.008040261, 0.13208859, 0.029760877], 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-0.24552552, 0.030037103, 0.04123544, 542 -0.11940523, 0.007358328, 0.1890978, 0.4833202, -0.34441817, 543 0.36312827, -0.26375428, 0.1457655, -0.19724406, 0.15548733], 544 545 projection_weights: [ 546 -0.009802181, 0.09401916, 0.0717386, -0.13895074, 0.09641832, 547 0.060420845, 0.08539281, 0.054285463, 0.061395317, 0.034448683, 548 -0.042991187, 0.019801661, -0.16840284, -0.015726732, -0.23041931, 549 -0.024478018, -0.10959692, -0.013875541, 0.18600968, -0.061274476, 550 0.0138165, -0.08160894, -0.07661644, 0.032372914, 0.16169067, 551 0.22465782, -0.03993472, -0.004017731, 0.08633481, -0.28869787, 552 0.08682067, 0.17240396, 0.014975425, 0.056431185, 0.031037588, 553 0.16702051, 0.0077946745, 0.15140012, 0.29405436, 0.120285, 554 -0.188994, -0.027265169, 0.043389652, -0.022061434, 0.014777949, 555 -0.20203483, 0.094781205, 0.19100232, 0.13987629, -0.036132768, 556 -0.06426278, -0.05108664, 0.13221376, 0.009441198, -0.16715929, 557 0.15859416, -0.040437475, 0.050779544, -0.022187516, 0.012166504, 558 0.027685808, -0.07675938, -0.0055694645, -0.09444123, 0.0046453946, 559 0.050794356, 0.10770313, -0.20790008, -0.07149004, -0.11425117, 560 0.008225835, -0.035802525, 0.14374903, 0.15262283, 0.048710253, 561 0.1847461, -0.007487823, 0.11000021, -0.09542012, 0.22619456, 562 -0.029149994, 0.08527916, 0.009043713, 0.0042746216, 0.016261552, 563 0.022461696, 0.12689082, -0.043589946, -0.12035478, -0.08361797, 564 -0.050666027, -0.1248618, -0.1275799, -0.071875185, 0.07377272, 565 0.09944291, -0.18897448, -0.1593054, -0.06526116, -0.040107165, 566 -0.004618631, -0.067624845, -0.007576253, 0.10727444, 0.041546922, 567 -0.20424393, 0.06907816, 0.050412357, 0.00724631, 0.039827548, 568 0.12449835, 0.10747581, 0.13708383, 0.09134148, -0.12617786, 569 -0.06428341, 0.09956831, 0.1208086, -0.14676677, -0.0727722, 570 0.1126304, 0.010139365, 0.015571211, -0.038128063, 0.022913318, 571 -0.042050496, 0.16842307, -0.060597885, 0.10531834, -0.06411776, 572 -0.07451711, -0.03410368, -0.13393489, 0.06534304, 0.003620307, 573 0.04490757, 0.05970546, 0.05197996, 0.02839995, 0.10434969, 574 -0.013699693, -0.028353551, -0.07260381, 0.047201227, -0.024575593, 575 -0.036445823, 0.07155557, 0.009672501, -0.02328883, 0.009533515, 576 -0.03606021, -0.07421458, -0.028082801, -0.2678904, -0.13221288, 577 0.18419984, -0.13012612, -0.014588381, -0.035059117, -0.04824723, 578 0.07830115, -0.056184657, 0.03277091, 0.025466874, 0.14494097, 579 -0.12522776, -0.098633975, -0.10766018, -0.08317623, 0.08594209, 580 0.07749552, 0.039474737, 0.1776665, -0.07409566, -0.0477268, 581 0.29323658, 0.10801441, 0.1154011, 0.013952499, 0.10739139, 582 0.10708251, -0.051456142, 0.0074137426, -0.10430189, 0.10034707, 583 0.045594677, 0.0635285, -0.0715442, -0.089667566, -0.10811871, 584 0.00026344223, 0.08298446, -0.009525053, 0.006585689, -0.24567553, 585 -0.09450807, 0.09648481, 0.026996298, -0.06419476, -0.04752702, 586 -0.11063944, -0.23441927, -0.17608605, -0.052156363, 0.067035615, 587 0.19271925, -0.0032889997, -0.043264326, 0.09663576, -0.057112187, 588 -0.10100678, 0.0628376, 0.04447668, 0.017961001, -0.10094388, 589 -0.10190601, 0.18335468, 0.10494553, -0.052095775, -0.0026118709, 590 0.10539724, -0.04383912, -0.042349473, 0.08438151, -0.1947263, 591 0.02251204, 0.11216432, -0.10307853, 0.17351969, -0.039091777, 592 0.08066188, -0.00561982, 0.12633002, 0.11335965, -0.0088127935, 593 -0.019777594, 0.06864014, -0.059751723, 0.016233567, -0.06894641, 594 -0.28651384, -0.004228674, 0.019708522, -0.16305895, -0.07468996, 595 -0.0855457, 0.099339016, -0.07580735, -0.13775392, 0.08434318, 596 0.08330512, -0.12131499, 0.031935584, 0.09180414, -0.08876437, 597 -0.08049874, 0.008753825, 0.03498998, 0.030215185, 0.03907079, 598 0.089751154, 0.029194152, -0.03337423, -0.019092513, 0.04331237, 599 0.04299654, -0.036394123, -0.12915532, 0.09793732, 0.07512415, 600 -0.11319543, -0.032502122, 0.15661901, 0.07671967, -0.005491124, 601 -0.19379048, -0.218606, 0.21448623, 0.017840758, 0.1416943, 602 -0.07051762, 0.19488361, 0.02664691, -0.18104725, -0.09334311, 603 0.15026465, -0.15493552, -0.057762887, -0.11604192, -0.262013, 604 -0.01391798, 0.012185008, 0.11156489, -0.07483202, 0.06693364, 605 -0.26151478, 0.046425626, 0.036540434, -0.16435726, 0.17338543, 606 -0.21401681, -0.11385144, -0.08283257, -0.069031075, 0.030635102, 607 0.010969227, 0.11109743, 0.010919218, 0.027526086, 0.13519906, 608 0.01891392, -0.046839405, -0.040167913, 0.017953383, -0.09700955, 609 0.0061885654, -0.07000971, 0.026893595, -0.038844477, 0.14543656], 610 611 projection_bias: [], 612} 613 614# Batch0: 4 (input_sequence_size) * 5 (n_input) 615input0[input] = [0.787926, 0.151646, 0.071352, 0.118426, 0.458058] 616# Batch1: 4 (input_sequence_size) * 5 (n_input) 617input0[input].extend( 618 [0.295743, 0.544053, 0.690064, 0.858138, 0.497181], 619) 620input0[cell_state_in] = [ 0 for _ in range(n_batch * n_cell) ] 621input0[output_state_in] = [ 0 for _ in range(n_batch * n_output) ] 622output0 = { 623 scratch_buffer: [ 0 for x in range(n_batch * n_cell * 4) ], 624 cell_state_out: [ 625 -0.0531632, -0.0118138, 0.0870833, 0.0347929, 626 -0.076144, -0.0659219, -0.0463811, 0.0141307, 627 -0.0127706, -0.03782, -0.00402401, -0.00571876, 628 -0.187957, -0.0247127, 0.0711425, 0.008244, 629 0.0492649, 0.126972, 0.0933097, 0.29848, 630 -0.0966178, -0.114417, 0.0387229, 0.0453255, 631 -0.181286, -0.0651251, -0.0996879, -0.00276995, 632 0.0617558, -0.0100728, 0.056304, -0.077416, 633 -0.162858, -0.0541251, 0.0571202, -0.0525331, 634 0.0724297, 0.171029, 0.141738, 0.295483, 635 ], 636 output_state_out: [ 637 -0.00396806, 0.029352, -0.00279226, 0.0159977, 638 -0.00835577, -0.0211779, 0.0283512, -0.0114597, 639 0.00907307, -0.0244004, -0.0152191, -0.0259063, 640 0.00914318, 0.00415119, 0.017147, 0.0134203, 641 -0.013869, 0.0287268, -0.00334694, 0.00733397, 642 -0.0287926, -0.0186926, 0.0193662, -0.0115437, 643 0.00422612, -0.0345232, 0.00223253, -0.00957321, 644 0.0210624, 0.013331, 0.0150954, 0.0216801 645 ], 646} 647# Batch0: 4 (input_sequence_size) * 16 (n_output) 648output0[output] = [ 649 -0.00396806, 0.029352, -0.00279226, 0.0159977, -0.00835576, 650 -0.0211779, 0.0283512, -0.0114597, 0.00907307, -0.0244004, 651 -0.0152191, -0.0259063, 0.00914318, 0.00415118, 0.017147, 652 0.0134203] 653# Batch1: 4 (input_sequence_size) * 16 (n_output) 654output0[output].extend( 655 [-0.013869, 0.0287268, -0.00334693, 0.00733398, -0.0287926, 656 -0.0186926, 0.0193662, -0.0115437, 0.00422612, -0.0345232, 657 0.00223253, -0.00957321, 0.0210624, 0.013331, 0.0150954, 658 0.02168], 659 ) 660 661Example((input0, output0)) 662