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create_vis.py
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create_vis.py
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import pickle
import matplotlib.pyplot as plt
from mlxtend.plotting import plot_confusion_matrix
res_RL_WN ={'CNN train_loss_list': [0.6958057284355164, 0.6930960416793823, 0.6926529407501221, 0.6918925046920776, 0.6914997100830078, 0.6895506381988525, 0.6869117617607117, 0.6820853352546692, 0.6773020625114441, 0.662178635597229, 0.6440454721450806, 0.6292213201522827, 0.5889046788215637, 0.5478882193565369, 0.4967164993286133, 0.4272269308567047, 0.3450983464717865, 0.25431182980537415, 0.16468729078769684, 0.09531116485595703, 0.05269724130630493, 0.03186358883976936, 0.02211790345609188, 0.016574757173657417, 0.013211310841143131, 0.011051603592932224, 0.009488808922469616, 0.00832605641335249, 0.007429172284901142, 0.006687203887850046], 'CNN train_accuracy_list': [0.49796875, 0.50890625, 0.51875, 0.51984375, 0.52546875, 0.53421875, 0.5428125, 0.5665625, 0.5734375, 0.60984375, 0.63140625, 0.6471875, 0.69703125, 0.73125, 0.77203125, 0.82625, 0.87578125, 0.92546875, 0.97171875, 0.9928125, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], 'MLP train_loss_list': [0.7008526921272278, 0.6782212853431702, 0.6566179394721985, 0.622295081615448, 0.5800589919090271, 0.5314801931381226, 0.47479331493377686, 0.4049808382987976, 0.32826438546180725, 0.2603800594806671, 0.1910039633512497, 0.1342652440071106, 0.09866520017385483, 0.07058674842119217, 0.050658781081438065, 0.03954479098320007, 0.032628439366817474, 0.027031034231185913, 0.023187657818198204, 0.020337803289294243, 0.018363863229751587, 0.016398757696151733, 0.015058020129799843, 0.013943632133305073, 0.012978218495845795, 0.012138644233345985, 0.011337105184793472, 0.010736116208136082, 0.010213668458163738, 0.009732622653245926], 'MLP train_accuracy_list': [0.510625, 0.5765625, 0.62296875, 0.66984375, 0.71328125, 0.7546875, 0.80015625, 0.84890625, 0.899375, 0.93625, 0.9671875, 0.98859375, 0.994375, 0.99890625, 0.99984375, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}
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res_WON_MLP_16000_samples = {'MLP train_loss_list': [0.3990882933139801, 0.2742719352245331, 0.21969278156757355, 0.18564364314079285, 0.16052024066448212, 0.13988302648067474, 0.12309224158525467, 0.10991652309894562, 0.09795497357845306, 0.08851198107004166, 0.08071582019329071, 0.07275184243917465, 0.06671749800443649, 0.06037455052137375, 0.05562056973576546, 0.0511409193277359, 0.04734039679169655, 0.04357440769672394, 0.04070030525326729, 0.0380094051361084, 0.03568623587489128, 0.03328554332256317, 0.03098461776971817, 0.029223607853055, 0.027717845514416695, 0.026427844539284706, 0.025174450129270554, 0.02373792603611946, 0.0228959321975708, 0.02214781939983368, 0.021396026015281677, 0.020664900541305542, 0.019869714975357056, 0.019090808928012848, 0.018563853576779366, 0.018206188455224037, 0.017677417024970055, 0.017062081024050713, 0.016866879537701607, 0.016483576968312263, 0.016142435371875763, 0.01582930237054825, 0.015565070323646069, 0.015177740715444088, 0.014977891929447651, 0.01485848892480135, 0.014656384475529194, 0.014429615810513496, 0.014172624796628952, 0.014132083393633366, 0.013957368209958076, 0.013839047402143478, 0.013618397526443005, 0.013485101982951164, 0.013323034159839153, 0.013310697861015797, 0.013260587118566036, 0.0130913769826293, 0.013094957917928696, 0.012973131611943245, 0.012799801304936409, 0.012751949951052666, 0.012688111513853073, 0.012606589123606682, 0.012567958794534206, 0.012452544644474983, 0.01247042790055275, 0.012433427385985851, 0.012323468923568726, 0.012242198921740055, 0.012311764992773533, 0.012212497182190418, 0.01218467764556408, 0.012068142183125019, 0.012079492211341858, 0.012027131393551826, 0.01202401239424944, 0.011968588456511497, 0.011963478289544582, 0.011931103654205799, 0.011942020617425442, 0.01184365525841713, 0.011835787445306778, 0.011716099455952644, 0.011788385920226574, 0.011776368133723736, 0.011781780049204826, 0.011703314259648323, 0.011716498993337154, 0.011705178767442703, 0.01165055576711893, 0.011650709435343742, 0.011611882597208023, 0.011616067960858345, 0.011595345102250576, 0.011533624492585659, 0.011547463946044445, 0.011479879729449749, 0.011509177275002003, 0.011476531624794006], 'MLP train_accuracy_list': [0.79546875, 0.8668359375, 0.8990234375, 0.9210546875, 0.9351953125, 0.9477734375, 0.9546875, 0.963203125, 0.9699609375, 0.9734375, 0.9773046875, 0.982109375, 0.9841015625, 0.986328125, 0.9891796875, 0.9903515625, 0.9919140625, 0.9933984375, 0.9941015625, 0.99484375, 0.9952734375, 0.99640625, 0.997421875, 0.997890625, 0.9982421875, 0.998515625, 0.9984375, 0.99890625, 0.9994140625, 0.9991796875, 0.9992578125, 0.999453125, 0.999609375, 0.999765625, 0.9998046875, 0.999609375, 0.999765625, 0.9998828125, 0.9998828125, 0.9998828125, 0.999921875, 1.0, 0.9998046875, 0.9999609375, 0.999921875, 1.0, 0.9999609375, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9998828125, 1.0, 1.0, 0.9999609375, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], 'MLP test_loss_list': [0.07818908244371414, 0.05904200300574303, 0.050236862152814865, 0.047754138708114624, 0.04241010546684265, 0.03669748827815056, 0.03283387050032616, 0.02972038835287094, 0.031094323843717575, 0.02507062442600727, 0.02442755363881588, 0.021633271127939224, 0.020194165408611298, 0.019709719344973564, 0.018253859132528305, 0.018002580851316452, 0.016644561663269997, 0.017421837896108627, 0.01691107451915741, 0.014957873150706291, 0.014612970873713493, 0.013551248237490654, 0.013260510750114918, 0.012850959785282612, 0.013223209418356419, 0.012339510954916477, 0.011537713930010796, 0.011791723780333996, 0.011468378826975822, 0.01137954369187355, 0.011033719405531883, 0.010469741187989712, 0.010426165536046028, 0.010175032541155815, 0.010223192162811756, 0.009678727015852928, 0.010106479749083519, 0.009831777773797512, 0.009539589285850525, 0.009340750053524971, 0.009520409628748894, 0.009615687653422356, 0.009666826575994492, 0.009836753830313683, 0.009932806715369225, 0.009103626012802124, 0.00947444699704647, 0.009176334366202354, 0.009505304507911205, 0.009423905983567238, 0.00908535998314619, 0.008865291252732277, 0.008877347223460674, 0.008868036791682243, 0.009318068623542786, 0.009053140878677368, 0.008920216001570225, 0.008998245000839233, 0.009045572951436043, 0.008688654750585556, 0.008720209822058678, 0.008774813264608383, 0.00841989554464817, 0.008491060696542263, 0.008485544472932816, 0.008700327947735786, 0.008961272425949574, 0.008579840883612633, 0.008627126924693584, 0.008769072592258453, 0.008479343727231026, 0.008351325988769531, 0.00859787967056036, 0.008465510793030262, 0.008853689767420292, 0.00825926847755909, 0.008546551689505577, 0.008367112837731838, 0.008218192495405674, 0.008779678493738174, 0.008281105197966099, 0.008574340492486954, 0.008141165599226952, 0.008327699266374111, 0.00818103551864624, 0.00842831376940012, 0.008168796077370644, 0.00805627554655075, 0.008364399895071983, 0.008203252218663692, 0.008071096614003181, 0.008112898096442223, 0.008159548044204712, 0.007986156269907951, 0.008231833577156067, 0.008155770599842072, 0.007922637276351452, 0.008461683988571167, 0.00806316640228033, 0.00830489955842495], 'MLP test_accuracy_list': [0.83109375, 0.88125, 0.90625, 0.9040625, 0.9140625, 0.93390625, 0.95109375, 0.9578125, 0.94828125, 0.9646875, 0.9634375, 0.9728125, 0.9746875, 0.97515625, 0.9775, 0.97671875, 0.97875, 0.976875, 0.976875, 0.98421875, 0.98484375, 0.98515625, 0.98625, 0.9853125, 0.98609375, 0.98546875, 0.98734375, 0.98734375, 0.98796875, 0.988125, 0.98765625, 0.9896875, 0.98890625, 0.98703125, 0.9890625, 0.98953125, 0.9890625, 0.98953125, 0.98953125, 0.99046875, 0.9890625, 0.988125, 0.99015625, 0.98765625, 0.98953125, 0.989375, 0.98859375, 0.98875, 0.988125, 0.98796875, 0.9896875, 0.98984375, 0.9890625, 0.989375, 0.98875, 0.98921875, 0.9890625, 0.989375, 0.98859375, 0.98875, 0.99, 0.98953125, 0.99046875, 0.99015625, 0.99, 0.98890625, 0.988125, 0.9896875, 0.98875, 0.98875, 0.99125, 0.98953125, 0.99015625, 0.989375, 0.98859375, 0.989375, 0.98921875, 0.99, 0.98953125, 0.99015625, 0.98953125, 0.98953125, 0.98953125, 0.989375, 0.99015625, 0.98828125, 0.99078125, 0.98921875, 0.9909375, 0.98984375, 0.99046875, 0.99015625, 0.9903125, 0.990625, 0.989375, 0.98953125, 0.9909375, 0.9903125, 0.99, 0.99046875]}
res_WON_MLP_8mil_params = {'MLP train_loss_list': [0.49670732021331787, 0.3899267911911011, 0.33669108152389526, 0.2968350946903229, 0.2723945379257202, 0.24554261565208435, 0.22655363380908966, 0.210834801197052, 0.1916857808828354, 0.17902863025665283, 0.16262269020080566, 0.15218955278396606, 0.14446616172790527, 0.13298234343528748, 0.12504200637340546, 0.11539353430271149, 0.10778859257698059, 0.1010395959019661, 0.094044990837574, 0.08879020810127258, 0.08393046259880066, 0.07722286880016327, 0.07328033447265625, 0.0689702108502388, 0.0649927482008934, 0.062349844723939896, 0.058200493454933167, 0.05473041534423828, 0.053062938153743744, 0.05127370357513428, 0.04729630425572395, 0.04555835947394371, 0.04311065375804901, 0.04121958091855049, 0.03942519798874855, 0.038572121411561966, 0.03626948595046997, 0.034969788044691086, 0.033231232315301895, 0.03191766142845154, 0.030883273109793663, 0.029809698462486267, 0.028885671868920326, 0.02766783908009529, 0.02716728113591671, 0.025986889377236366, 0.025277812033891678, 0.024224018678069115, 0.023489587008953094, 0.022774992510676384], 'MLP train_accuracy_list': [0.71825, 0.8, 0.83825, 0.875, 0.88175, 0.89825, 0.911, 0.91825, 0.935, 0.93775, 0.954, 0.95325, 0.962, 0.96625, 0.97475, 0.977, 0.978, 0.98325, 0.98475, 0.9875, 0.98725, 0.9905, 0.9935, 0.99325, 0.9965, 0.997, 0.99725, 0.997, 0.997, 0.99625, 0.99775, 0.999, 0.999, 0.99875, 0.99925, 0.999, 1.0, 0.99975, 1.0, 1.0, 0.99975, 1.0, 1.0, 1.0, 0.99975, 0.99975, 1.0, 1.0, 1.0, 1.0], 'MLP test_loss_list': [0.10892117768526077, 0.09399281442165375, 0.08815725892782211, 0.08046029508113861, 0.07811912894248962, 0.07248814404010773, 0.06680305302143097, 0.06552039831876755, 0.06526891142129898, 0.0587029904127121, 0.05854988470673561, 0.05463942140340805, 0.054656531661748886, 0.054951004683971405, 0.052654046565294266, 0.052253711968660355, 0.05152495205402374, 0.05080506578087807, 0.049073390662670135, 0.04886417090892792, 0.050136130303144455, 0.04728711396455765, 0.04807357117533684, 0.04672723263502121, 0.05057892948389053, 0.04560812935233116, 0.04568123817443848, 0.04626396298408508, 0.04565279558300972, 0.04416356980800629, 0.04494880884885788, 0.04452921450138092, 0.04466911777853966, 0.047643356025218964, 0.04561717435717583, 0.044634878635406494, 0.044769566506147385, 0.04555751383304596, 0.04545806720852852, 0.044404037296772, 0.04647282510995865, 0.04376761615276337, 0.04467320069670677, 0.04542871564626694, 0.04575492441654205, 0.04428545758128166, 0.04443797096610069, 0.04407856613397598, 0.046090204268693924, 0.04393552243709564], 'MLP test_accuracy_list': [0.763, 0.769, 0.783, 0.83, 0.819, 0.827, 0.859, 0.854, 0.859, 0.894, 0.887, 0.893, 0.892, 0.891, 0.89, 0.898, 0.9, 0.899, 0.902, 0.914, 0.911, 0.908, 0.913, 0.914, 0.903, 0.918, 0.908, 0.911, 0.915, 0.918, 0.909, 0.913, 0.912, 0.911, 0.913, 0.919, 0.918, 0.913, 0.915, 0.919, 0.917, 0.915, 0.918, 0.91, 0.913, 0.918, 0.918, 0.919, 0.917, 0.915]}
print(res_WON["CNN test_accuracy_list"])
plt.plot(res_WON_MLP_16000_samples["MLP test_accuracy_list"][:50], c="red", label="MLP with 16k samples")
plt.xlabel("Epochs")
plt.ylabel("Accuracy")
plt.legend()
plt.plot(res_WON_MLP_8mil_params["MLP test_accuracy_list"], c="purple", label="Over-parametrized MLP")
plt.legend()
plt.ylim([0.75,1.0])
plt.plot(res_WON["MLP test_accuracy_list"], c="blue", label="MLP")
plt.xlabel("Epochs")
plt.ylabel("Accuracy")
plt.legend()
plt.plot(res_WON["CNN test_accuracy_list"], c="green", label="CNN")
plt.legend()
# plt.plot(res_WN["CNN train_accuracy_list"], c="green", label="CNN on data with noise")
# plt.legend()
# plt.plot(res_WON["CNN train_accuracy_list"], c="purple", label="CNN on data without noise")
# plt.legend()
plt.show()
# plt.savefig(f'comparison test accuracy.png')