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#include "DeepNeuralNetwork.h"
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// 7x7 default single weight matrix of conv2D filter
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/*
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float conv2D_filter[CONV2D_SIZE][CONV2D_SIZE] = {
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{-0.43029568, -0.39133656, -0.18070924, 1.9146448, -0.72108614, -0.06065637, -0.5327825},
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{0.42427886, 0.48808837, -0.2642057, 0.3576343, 0.6267088, 0.21197554, 0.86757374},
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{0.0244696, -0.04827072, 0.16628812, -0.17212549, -0.13450626, 0.63747036, 1.4266869},
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{-0.1043217, -0.3071339, -0.15082423, 0.32923478, 0.26912355, 0.07193143, -0.12325906},
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{-0.02518349, -0.55556524, -0.38330624, -0.9799975, -0.77819675, -0.53853893, -0.6251154},
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{-0.75074315, -1.0719602, -1.2721115, -0.33550426, -1.4457195, -2.9715965, -0.2728055},
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{2.3081367, -0.12951809, -0.26579207, -1.0174242, -0.32230973, 1.5067326, -2.0501044}
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};
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*/
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// NEW
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float conv2D_filter[CONV2D_SIZE][CONV2D_SIZE] = {
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{ -0.49382326006889343, 0.5100185871124268, -0.0487382672727108, -0.009719527326524258, 0.005600391887128353, 2.2946598529815674, -1.1213185 },
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{ 0.13655433058738708, -0.5743462443351746, 0.19815398752689362, 0.2750433683395386, 0.010824864730238914, -0.1927724927663803, 3.3371546 },
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{ -0.12162114679813385, 0.23516184091567993, -0.2419496774673462, -0.4832926392555237, -0.6142756938934326, 0.4416441023349762, -2.010287 },
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{ -0.34879812598228455, 0.08016864210367203, -0.7465101480484009, -1.8006373643875122, -1.5648049116134644, 0.9341891407966614, -0.030018441 },
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{ -0.31813231110572815, -0.4452771544456482, 0.5476454496383667, 0.008089866489171982, -1.8979229927062988, -1.6400198936462402, 0.7545687 },
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{ 1.183456301689148, -0.08245204389095306, 0.7799625396728516, -0.4993128478527069, -1.986671805381775, -1.3938006162643433, -1.1643082 },
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{ 2.2604691982269287, -2.136565685272217, 0.9039211869239807, 1.2003583908081055, -0.9059545397758484, -1.3128386735916138, -2.3470676 }
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};
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// 121x36 default single weight vector of FullyConnected layer
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//const float dense_weights[DENSE_WEIGHTS_LEN] = {0.42389297, -0.3777679, -0.14339094, -0.51132095, -0.096901745, 0.38238993, -0.18748926, -0.05787523, -0.37740272, -0.41629294, 0.488402, 1.1407253, 0.18530186, 0.20089737, 0.89263374, 0.45293847, 0.44526452, 0.010291849, -0.71538967, 2.6833496, 0.30658486, 0.5436105, -0.59970874, -0.12296562, 0.19900385, 0.11223487, 0.40871108, 0.114251114, -0.3096374, -0.20738089, -0.06686727, -0.19470724, -0.6774756, -0.15291542, 0.0899597, -0.22740205, -0.22951202, 0.60986954, 0.04057434, -0.3066007, 0.11805771, -0.24862391, -0.70913166, 0.27321497, -0.11015183, -0.021791615, 2.8757343, 0.59044534, 0.44518927, 1.0691243, 0.8508363, 0.7874608, 0.80193627, 0.5716211, -0.59050417, -1.7909682, -0.25483596, 1.1381904, -0.08203808, 0.96682405, 1.1616689, 1.0529191, 0.2436091, 0.2542588, 0.3740252, 0.517982, 0.6528926, 0.38528642, 2.475672, 0.41712716, 0.06866825, -0.89877963, 0.05766443, 0.15344718, 0.31257457, 0.27826336, 0.39848238, -0.4305429, -0.52350044, 0.69019383, -0.016317429, -0.117210016, 2.1307664, 0.17660204, 0.39224806, -2.4110136, -0.033722606, 0.47478706, 0.33740273, 0.5499651, 0.24095415, -0.6782214, -0.71320057, 1.2701788, -0.14626218, 1.0203218, 0.06266565, 0.043855615, -0.16040483, 0.9620783, 0.6608588, -0.49670747, 0.24660859, 0.48417103, -0.98145175, 0.46714437, -0.061174504, -0.70336527, -0.094014846, -0.025612786, 0.15698612, 0.18573503, 0.38523254, -0.20984864, -0.157599, 0.59265226, -0.13553762, -0.15115193, -0.2590725, 0.092004165, 0.654726, -1.5012388, 0.057761177, 0.66090214, -0.4401016, -0.23698848, -0.48506936, 1.091287, 1.9044684, 0.6888644, -0.6719549, 0.04590096, -0.45969805, -0.74753857, -0.53330606, 0.56197846, 0.45607266, -0.9352503, 2.194364, 0.6796579, -0.14293773, 0.5946347, 2.8160102, -0.8703773, -0.0037569553, -0.12519003, 0.10164239, 0.06708348, 0.4169359, -0.03453148, 0.12676106, 0.35547408, -0.072755, 0.025682516, 0.5952433, 0.72976744, -0.041536942, -2.611844, -1.0706439, -1.434097, 1.2804911, 0.33062544, 0.4972964, -1.1935048, -0.5844118, 0.45473248, -0.8400934, -0.36910495, -1.5178102, -2.8285313, 0.31663215, 0.35124347, 0.7048184, -0.8470644, -0.3396202, 0.47630113, 1.275406, 2.3836675, 0.1699988, -1.3107492, 0.13029324, -0.11459066, -0.03743892, 0.009061946, 0.44175643, 0.11334918, 0.2537384, 0.33573765, 0.015218589, 0.08340328, -1.2001379, 1.0493072, 1.6820279, -0.15743437, -1.557911, -1.7414311, -1.685601, 0.2614582, 0.4484806, -0.60651267, -0.38393772, 0.29801595, -0.3498166, -0.18517497, -1.6250647, 1.2020968, 0.30855483, 0.68434894, -0.60668373, -1.0258394, 1.5870657, 0.45468715, -0.40529406, -0.18614824, 1.9069281, -0.7463413, 0.10169048, -0.016515123, -0.14809036, -0.021974182, 0.38245893, 0.13202217, 0.1089799, 0.22860067, 0.06288065, 0.31156933, -0.2630527, 0.20787434, -1.4027474, 0.501361, 0.19924887, -2.3358798, 0.48173675, -0.62690514, -1.2441205, -0.8232389, 1.4678631, -1.1262559, 0.05690427, 0.971059, -2.1614606, 0.6091414, -0.2070127, 0.593261, -0.23767048, -1.9171128, 0.6544346, -2.6813555, -0.5858414, 0.57390565, -0.111194514, -0.8224281, 0.16765165, 0.24888536, 0.010621272, 0.15801257, 0.13963982, 0.06285063, 0.10110444, 0.31619918, 0.23847148, 0.4719953, -0.098805316, 0.109896965, 0.28437927, 0.42254564, -0.44896218, -1.8472936, -2.579797, -0.5199091, -1.5056546, -0.53144354, -2.0045843, -0.9591429, -1.5721201, -1.1860585, -2.2555275, 0.6858286, -0.062898554, 0.277558, -0.06704853, -1.7779754, -0.7552676, -1.8527445, -0.74111176, 0.7426416, -2.1489856, -1.4497088, 0.21511655, 0.29493427, 0.19277693, 0.19166152, 0.052070126, 0.045550406, -0.118257366, 0.42802668, 0.24750032, 0.4359924, -2.2338843, -0.19839637, 0.50054526, 0.5414637, 0.46819708, -1.8780116, -2.483511, -1.3811833, -2.9746706, 0.09886132, -0.5891412, -1.3276248, -1.9627063, -2.2081769, -1.7113664, 0.57645375, -0.05607187, -1.4437557, 0.29212928, -1.9565529, -1.1142669, -2.1146207, -1.2988743, -2.604722, 0.5732981, -1.8462738, 0.2502249, 0.02065625, 0.26285538, 0.28697094, 0.21941763, 0.08491357, -0.19631414, 0.5496883, 0.29807723, 0.4690562, -2.5211508, 0.44373193, -2.08439, 0.6684532, -3.2619002, -2.906165, -2.5045145, -1.9562882, -3.0003183, -0.22861914, -2.978625, -2.4302516, -0.4334013, 0.71262974, -1.386678, -2.1464713, 0.07873577, -1.6701168, -3.169167, -2.732133, -2.3369753, -2.2762775, -2.3067713, 0.632718, -3.243806, -2.2171512, 0.40856364, 0.13975564, 0.36570984, 0.41774112, 0.38069272, 0.15554284, -0.43195772, 0.6596637, 0.44486395, 0.6561163, -2.5488458, 0.8503141, -2.7461896, -3.912092, -2.9320664, -0.07219526, -3.087168, -3.2305408, -3.2410069, -0.61080503, -2.5809364, -3.558956, -2.413043, -7.1395535, -3.0406265, -3.2122667, -2.7143598, -2.246922, -3.0943594, -2.6532943, -4.078776, -2.3170002, -0.16941176, -3.4395905, -2.4938374, -2.5914423, -0.5347727, -0.15484881, -0.14384705, -0.14319764, -0.42223513, -0.32197085, -0.22621892, 0.06640142, -0.27687535, -0.16321217, -2.3993156, 1.7354808, 0.45093855, 1.8852547, 1.8448536, 1.1437777, 0.8213022, 1.2161463, 2.0815344, -1.098352, 2.4597185, 1.7835237, 1.1611048, 2.0342765, 1.3404254, 0.41407135, 0.008882063, -0.2639584, 1.0315077, 1.301822, 1.600877, 0.32033488, -1.2535053, 0.25913036, 0.7906703, 1.7392217, -0.38242584, -0.36454895, 0.2022011, -0.14577766, -0.14141057, -0.50433296, -0.4330855, 0.115819156, -0.25745842, -0.24487501, -1.4470307, 0.76873744, -0.07450827, -3.1628437, -0.40631646, 1.2168791, 0.13730457, -1.5436051, 2.6327715, -0.16308774, 0.056463994, -0.59173155, 1.7775297, 1.4386164, 0.5009179, 1.79348, 0.7166077, 0.64359546, 0.17667012, 0.8255079, -0.37755275, 0.3032599, 1.1292356, -0.14416918, 0.38805157, 0.09402936, -0.4181205, -0.044758815, 0.2002143, 0.2060625, -0.15531185, -0.38485745, -0.33362642, 0.120972216, -0.26449266, -0.22760399, -2.081246, 0.21205935, -1.0798072, -0.08863829, -1.1600833, -2.3980978, -1.2255846, -1.2377771, -0.24234359, -0.8213919, 1.1118027, 0.014415595, 1.2270523, 0.969612, -1.8029566, -2.5801334, -1.052527, -0.9669329, -0.9494282, -0.6193145, -1.6521844, 2.5283327, 0.873752, 1.6697481, 1.9811404, 0.479945, -0.28041124, -0.087885946, 0.06373004, 0.070942596, -0.02389323, -0.3961188, -0.1873861, 0.054316282, -0.31258526, -0.12898418, -1.26638, -0.3042685, -0.12554644, 0.12239892, -1.647372, -1.690219, -0.29266295, 0.864653, 0.18062605, 1.1471069, 0.5674665, 1.3976731, 0.7355725, 1.0824572, -2.0786376, -2.2164156, -0.76821625, -0.73049533, -0.09660139, -1.1656381, 0.9296716, 0.7811517, 0.788602, 0.271739, 0.97152036, -1.1114271, -0.09446471, -0.17294721, -0.0031538098, -0.07226013, 0.04584039, -0.2606633, -0.041472204, -0.08161976, -0.18933576, -0.34952086, 0.18864578, 0.30259067, -0.15312387, 0.5035197, -1.053902, -0.16423434, 0.75435144, 1.436215, 1.7228459, -0.5173778, 0.87329006, 1.4290867, -0.079792954, -0.759557, -1.6211039, 0.61165833, 0.07024823, 0.63462573, -0.4523277, 0.20301324, -1.2009947, 0.10435102, 0.09087844, 0.6447474, -0.49906668, 0.8955731, -0.032014903, -0.14581755, 0.07575, -0.16263527, 0.14666295, -0.008640405, 0.068136595, -0.1307431, -0.062141947, -0.4443844, 0.37721208, 0.23750693, 0.5713232, 0.37071392, -0.36383343, 0.62689644, -1.290416, -0.7206118, -1.490483, 0.6975459, 0.6465952, 0.33244336, 0.6453418, 1.0062888, 0.27449656, 0.49901977, -0.2344083, 0.61453676, -0.7352109, -1.4411569, 0.6427453, 1.7045765, 0.81907225, 0.23802495, 1.2425176, -0.30887452, -0.07759598, -0.15623397, 0.01889215, -0.25829634, 0.08559114, 0.016869362, 0.16412514, -0.06982697, -0.03547665, -0.30469605, 0.58089226, 0.19230358, -0.5018553, 0.48751697, 0.14398557, 0.29641291, 0.3362131, 0.87484473, 0.23656642, -0.6313914, -0.37684345, 0.65472955, 0.71539235, -1.1580848, -0.7316568, 0.51332134, -0.53132194, 0.67392445, 0.11918988, -1.0789347, 0.43228957, -1.0096008, 0.7523026, 0.0022442697, 0.5997219, 0.16828375, -0.17876439, 0.067574434, -0.15589665, -0.28632188, -0.340393, -0.015856992, 0.1849889, -0.12760733, -0.150882, -0.09697137, 0.34205735, -0.00962641, 0.50589085, 0.4908124, 0.38270736, 0.5717444, 0.50347275, -0.2492529, 1.0912802, 0.10654226, -1.6232108, -1.8414494, -1.3166503, 0.7144374, 0.22994445, 0.9048704, 0.2124093, 0.4612855, -0.05018068, 0.3345483, -0.3689101, 0.5251076, -0.5017565, 0.23701707, -2.7430756, 0.18692434, -0.22197177, 0.0793638, -0.09269587, -0.073667794, -0.22925808, -0.09126595, 0.07636182, 0.20410699, -0.15298614, 0.16718173, 0.42802933, -0.18622091, -0.007218228, -0.60177124, -0.008170811, 0.54176956, -0.122483045, 0.24999985, 0.0031662742, -0.026935881, -0.40447176, -1.7683866, -0.018825602, -0.65580076, 0.27198887, 0.36191362, 0.16916682, -0.011786537, -0.20343232, 0.47943696, 0.22028294, 0.17800179, 0.21789655, -0.1882127, 0.10301607, 0.33697283, 0.024332289, -0.31704614, 0.027071789, -0.087452754, -0.018579952, -0.09775386, -0.10094883, 0.19743145, 0.021248497, 0.22525409, 0.36435118, -1.1116644, -1.3968927, 0.4581955, -0.3105869, 0.36368036, -1.6193311, 0.40515167, -0.6299937, 0.15131398, 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5:2dce07562de8
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| pepperu96 |
5:2dce07562de8
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31
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// NEW
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| pepperu96 |
5:2dce07562de8
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32
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const float dense_weights[DENSE_WEIGHTS_LEN] = { 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};
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33
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5:2dce07562de8
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34
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DeepNeuralNetwork::DeepNeuralNetwork() {
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5:2dce07562de8
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35
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}
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5:2dce07562de8
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36
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| pepperu96 |
5:2dce07562de8
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37
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/**
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38
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*
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5:2dce07562de8
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39
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* @param image
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5:2dce07562de8
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40
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* @return an integer between 0 and 35 containing the prediction
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5:2dce07562de8
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41
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*/
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5:2dce07562de8
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42
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5:2dce07562de8
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43
|
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| pepperu96 |
5:2dce07562de8
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44
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int DeepNeuralNetwork::inference(const int image[][INPUT_IMAGE_SIZE]) {
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5:2dce07562de8
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45
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float input[INPUT_IMAGE_SIZE][INPUT_IMAGE_SIZE];
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| pepperu96 |
5:2dce07562de8
|
46
|
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| pepperu96 |
5:2dce07562de8
|
47
|
for (int i = 0; i < INPUT_IMAGE_SIZE; i++)
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| pepperu96 |
5:2dce07562de8
|
48
|
for (int j = 0; j < INPUT_IMAGE_SIZE; j++)
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| pepperu96 |
5:2dce07562de8
|
49
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input[i][j] = image[i][j];
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5:2dce07562de8
|
50
|
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| pepperu96 |
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51
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conv2d(input, conv2D_filter, conv2d_output);
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5:2dce07562de8
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52
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maxpool2d(conv2d_output, maxpool_output);
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| pepperu96 |
5:2dce07562de8
|
53
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flatten(maxpool_output, flatten_output);
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| pepperu96 |
5:2dce07562de8
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54
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dense(flatten_output, dense_output);
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| pepperu96 |
5:2dce07562de8
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55
|
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| pepperu96 |
5:2dce07562de8
|
56
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float max = dense_output[0];
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57
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int maxIndex = 0;
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5:2dce07562de8
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58
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for (int i = 0; i < DENSE_OUTPUT_SIZE; i++) {
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5:2dce07562de8
|
59
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if (dense_output[i] > max) {
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5:2dce07562de8
|
60
|
max = dense_output[i];
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| pepperu96 |
5:2dce07562de8
|
61
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maxIndex = i;
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| pepperu96 |
5:2dce07562de8
|
62
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}
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| pepperu96 |
5:2dce07562de8
|
63
|
}
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| pepperu96 |
5:2dce07562de8
|
64
|
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| pepperu96 |
5:2dce07562de8
|
65
|
for (int i = 0; i < DENSE_OUTPUT_SIZE; i++) {
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5:2dce07562de8
|
66
|
// std::cout << "Neuron " << i << ": " << dense_output[i] << std::endl;
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|
67
|
}
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| pepperu96 |
5:2dce07562de8
|
68
|
char prediction;
|
| pepperu96 |
5:2dce07562de8
|
69
|
if (maxIndex < 10)
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| pepperu96 |
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|
70
|
prediction = maxIndex + '0';
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| pepperu96 |
5:2dce07562de8
|
71
|
else
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| pepperu96 |
5:2dce07562de8
|
72
|
prediction = maxIndex - 10 + 'A';
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| pepperu96 |
5:2dce07562de8
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73
|
//std::cout << "Inference neuron: " << maxIndex << std::endl;
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|
74
|
//std::cout << "Inference letter: " << prediction << std::endl;
|
| pepperu96 |
5:2dce07562de8
|
75
|
//std::cout << std::endl;
|
| pepperu96 |
5:2dce07562de8
|
76
|
return maxIndex;
|
| pepperu96 |
5:2dce07562de8
|
77
|
}
|
| pepperu96 |
5:2dce07562de8
|
78
|
|
| pepperu96 |
5:2dce07562de8
|
79
|
void DeepNeuralNetwork::conv2d(const float input[][CONV2D_INPUT_SIZE], const float kernel[][CONV2D_SIZE],
|
| pepperu96 |
5:2dce07562de8
|
80
|
float output[][CONV2D_OUTPUT_SIZE]) {
|
| pepperu96 |
5:2dce07562de8
|
81
|
for (int i = 0; i < CONV2D_OUTPUT_SIZE; i++) {
|
| pepperu96 |
5:2dce07562de8
|
82
|
for (int j = 0; j < CONV2D_OUTPUT_SIZE; j++) {
|
| pepperu96 |
5:2dce07562de8
|
83
|
float res = 0;
|
| pepperu96 |
5:2dce07562de8
|
84
|
for (int k = 0; k < CONV2D_SIZE; k++) {
|
| pepperu96 |
5:2dce07562de8
|
85
|
for (int m = 0; m < CONV2D_SIZE; m++) {
|
| pepperu96 |
5:2dce07562de8
|
86
|
int row = i + k;
|
| pepperu96 |
5:2dce07562de8
|
87
|
int col = j + m;
|
| pepperu96 |
5:2dce07562de8
|
88
|
float mul = input[row][col] * kernel[k][m];
|
| pepperu96 |
5:2dce07562de8
|
89
|
res = res + mul;
|
| pepperu96 |
5:2dce07562de8
|
90
|
}
|
| pepperu96 |
5:2dce07562de8
|
91
|
}
|
| pepperu96 |
5:2dce07562de8
|
92
|
output[i][j] = leaky_relu(res);
|
| pepperu96 |
5:2dce07562de8
|
93
|
}
|
| pepperu96 |
5:2dce07562de8
|
94
|
}
|
| pepperu96 |
5:2dce07562de8
|
95
|
}
|
| pepperu96 |
5:2dce07562de8
|
96
|
|
| pepperu96 |
5:2dce07562de8
|
97
|
void DeepNeuralNetwork::maxpool2d(const float input[][MAX_POOL_INPUT_SIZE], float output[][MAX_POOL_OUTPUT_SIZE]) {
|
| pepperu96 |
5:2dce07562de8
|
98
|
for (int i = 0; i < MAX_POOL_OUTPUT_SIZE; i++)
|
| pepperu96 |
5:2dce07562de8
|
99
|
for (int j = 0; j < MAX_POOL_OUTPUT_SIZE; j++)
|
| pepperu96 |
5:2dce07562de8
|
100
|
output[i][j] = 0;
|
| pepperu96 |
5:2dce07562de8
|
101
|
|
| pepperu96 |
5:2dce07562de8
|
102
|
for (int y = 0; y < MAX_POOL_OUTPUT_SIZE; ++y) {
|
| pepperu96 |
5:2dce07562de8
|
103
|
for (int x = 0; x < MAX_POOL_OUTPUT_SIZE; ++x) {
|
| pepperu96 |
5:2dce07562de8
|
104
|
for (int i = 0; i < MAX_POOL_SIZE; ++i) {
|
| pepperu96 |
5:2dce07562de8
|
105
|
for (int j = 0; j < MAX_POOL_SIZE; ++j) {
|
| pepperu96 |
5:2dce07562de8
|
106
|
float value = input[y * MAX_POOL_SIZE + i][x * MAX_POOL_SIZE + j];
|
| pepperu96 |
5:2dce07562de8
|
107
|
output[y][x] = std::max(output[y][x], value);
|
| pepperu96 |
5:2dce07562de8
|
108
|
}
|
| pepperu96 |
5:2dce07562de8
|
109
|
}
|
| pepperu96 |
5:2dce07562de8
|
110
|
}
|
| pepperu96 |
5:2dce07562de8
|
111
|
}
|
| pepperu96 |
5:2dce07562de8
|
112
|
}
|
| pepperu96 |
5:2dce07562de8
|
113
|
|
| pepperu96 |
5:2dce07562de8
|
114
|
void DeepNeuralNetwork::flatten(const float input[][FLATTEN_INPUT_SIZE], float *output) {
|
| pepperu96 |
5:2dce07562de8
|
115
|
for (int i = 0; i < FLATTEN_INPUT_SIZE; i++)
|
| pepperu96 |
5:2dce07562de8
|
116
|
for (int j = 0; j < FLATTEN_INPUT_SIZE; j++)
|
| pepperu96 |
5:2dce07562de8
|
117
|
output[i * FLATTEN_INPUT_SIZE + j] = input[i][j];
|
| pepperu96 |
5:2dce07562de8
|
118
|
}
|
| pepperu96 |
5:2dce07562de8
|
119
|
|
| pepperu96 |
5:2dce07562de8
|
120
|
void DeepNeuralNetwork::dense(const float *input, float *output) {
|
| pepperu96 |
5:2dce07562de8
|
121
|
for (int i = 0; i < DENSE_OUTPUT_SIZE; i++)
|
| pepperu96 |
5:2dce07562de8
|
122
|
output[i] = 0;
|
| pepperu96 |
5:2dce07562de8
|
123
|
|
| pepperu96 |
5:2dce07562de8
|
124
|
for (int i = 0; i < DENSE_INPUT_SIZE; i++)
|
| pepperu96 |
5:2dce07562de8
|
125
|
for (int j = 0; j < DENSE_OUTPUT_SIZE; j++)
|
| pepperu96 |
5:2dce07562de8
|
126
|
output[j] += dense_weights[i * DENSE_OUTPUT_SIZE + j] * input[i];
|
| pepperu96 |
5:2dce07562de8
|
127
|
}
|
| pepperu96 |
5:2dce07562de8
|
128
|
|
| pepperu96 |
5:2dce07562de8
|
129
|
float DeepNeuralNetwork::leaky_relu(float x) {
|
| pepperu96 |
5:2dce07562de8
|
130
|
if (x >= 0)
|
| pepperu96 |
5:2dce07562de8
|
131
|
return x;
|
| pepperu96 |
5:2dce07562de8
|
132
|
return 0.01 * x;
|
| pepperu96 |
5:2dce07562de8
|
133
|
}
|
| pepperu96 |
5:2dce07562de8
|
134
|
|
| pepperu96 |
5:2dce07562de8
|
135
|
char DeepNeuralNetwork::label_to_char(int label) {
|
| pepperu96 |
5:2dce07562de8
|
136
|
if (label < 10)
|
| pepperu96 |
5:2dce07562de8
|
137
|
return label + '0';
|
| pepperu96 |
5:2dce07562de8
|
138
|
return label - 10 + 'A';
|
| pepperu96 |
5:2dce07562de8
|
139
|
}
|
| pepperu96 |
5:2dce07562de8
|
140
|
|
Lorenzo Montalto
