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accES.py
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import matplotlib.pyplot as plt
import numpy as np
import matplotlib.pylab as pylab
params = {'legend.fontsize': 'large',
'axes.labelsize': 'large',
'axes.titlesize':'x-large'}
pylab.rcParams.update(params)
diffuserun_timeonly_loss=[2.7122268676757812, 1.7658891677856445, 1.3685306310653687, 1.2012358903884888, 1.1301476955413818, 1.0992767810821533, 1.0850846767425537, 1.0780483484268188, 1.0738497972488403, 1.0710564851760864]
diffuserun_timeonly_acc=[0.3608400821685791, 0.3714863359928131, 0.3805151879787445, 0.38627859950065613, 0.38926663994789124, 0.3926195204257965, 0.3948958218097687, 0.3974754214286804, 0.39916330575942993, 0.4005577862262726]
diffuserun_timeonly_custom=[-0.7186515927314758, -1.473449945449829, -2.6349451541900635, -4.239582061767578, -5.220722675323486, -4.19801139831543, -10.777006149291992, np.inf, -5.437722206115723, -8.105989456176758]
diffuserun_allparams_loss=[2.7322967052459717, 1.8040478229522705, 1.4036529064178467, 1.2291523218154907, 1.1514133214950562, 1.1152474880218506, 1.0975168943405151, 1.087656855583191, 1.0812894105911255, 1.0763003826141357, 1.0721664428710938]
diffuserun_allparams_acc=[0.3717462122440338, 0.3773043155670166, 0.3811073303222656, 0.38369524478912354, 0.38731661438941956, 0.38997402787208557, 0.39219003915786743, 0.39543864130973816, 0.3972305953502655, 0.40068480372428894, 0.4030703604221344]
diffuserun_allparams_custom=[-0.7098720669746399, -1.4120745658874512, -2.253985643386841, -2.232088565826416, 4.1212897300720215, -5.795780181884766, -6.658145904541016, -20.715091705322266, 5.984277725219727, -7.478765487670898, 15.003805160522461]
diffuserun_shilon_loss=[2.720522403717041, 1.7774231433868408, 1.3817042112350464, 1.2155512571334839, 1.1453535556793213, 1.115229606628418, 1.1020811796188354, 1.0960962772369385, 1.0931119918823242, 1.0914349555969238, 1.0902785062789917]
diffuserun_shilon_acc=[0.34063059091567993, 0.3430972695350647, 0.34596219658851624, 0.34918975830078125, 0.35201695561408997, 0.3561128079891205, 0.3576790690422058, 0.36041292548179626, 0.36172908544540405, 0.3634123206138611, 0.3657827079296112]
diffuserun_shilon_custom=[-0.7108279466629028, -1.4398059844970703, -2.542045831680298, -3.8334593772888184, -5.235379695892334, -5.608896255493164, -5.755305290222168, -5.921659469604492, -2.713362455368042, -4.433699131011963, -4.717963695526123]
diffuserun_chargetime_loss=[2.733572483062744, 1.7985658645629883, 1.3982131481170654, 1.2266589403152466, 1.1530424356460571, 1.1214289665222168, 1.1077747344970703, 1.1018636226654053, 1.0992170572280884, 1.0980803966522217, 1.0975369215011597, 1.0972460508346558, 1.097138524055481, 1.0970079898834229]
diffuserun_chargetime_acc=[0.3394152522087097, 0.34108516573905945, 0.34129902720451355, 0.3412405252456665, 0.34215351939201355, 0.3415174186229706, 0.3416179418563843, 0.3419346511363983, 0.3425966203212738, 0.3426322638988495, 0.3431908190250397, 0.34348171949386597, 0.3431193232536316, 0.3436996042728424]
diffuserun_chargetime_custom=[-0.7042309045791626, -1.4027239084243774, -2.4518814086914062, -3.6467268466949463, -4.453972339630127, -5.31343412399292, -1.6822130680084229, -5.722330093383789, -6.156676769256592, -5.621947765350342, -6.378216743469238, -5.7491350173950195, -5.367465972900391, -3.368389844894409]
fig = plt.figure(figsize=(16,7))
plt.subplot(1, 3, 1)
plt.plot(diffuserun_timeonly_acc, label='Method A (ES)')
plt.plot(diffuserun_allparams_acc, label='Method B (ES)')
plt.plot(diffuserun_shilon_acc, label='Method C (ES)')
plt.plot(diffuserun_chargetime_acc, label='Method D (ES)')
plt.title('Diffuse Run (ES)')
plt.ylabel('Categorical Accuracy')
plt.xlabel('Epoch')
plt.legend(loc='upper right')
plt.subplot(1, 3, 2)
plt.plot(diffuserun_timeonly_loss, label='Method A (ES)')
plt.plot(diffuserun_allparams_loss, label='Method B (ES)')
plt.plot(diffuserun_shilon_loss, label='Method C (ES)')
plt.plot(diffuserun_chargetime_loss, label='Method D (ES)')
plt.title('Diffuse Run (ES)')
plt.ylabel('Categorical Crossentropy Loss')
plt.xlabel('Epoch')
plt.legend(loc='upper right')
plt.subplot(1, 3, 3)
plt.plot(diffuserun_timeonly_custom, label='Method A (ES)')
plt.plot(diffuserun_allparams_custom, label='Method B (ES)')
plt.plot(diffuserun_shilon_custom, label='Method C (ES)')
plt.plot(diffuserun_chargetime_custom, label='Method D (ES)')
plt.title('Diffuse Run (ES)')
plt.ylabel('Custom Metric')
plt.xlabel('Epoch')
plt.legend(loc='upper right')
plt.tight_layout()
plt.savefig('trainlogES2.png',dpi=300)