1327 lines
35 KiB
Python
1327 lines
35 KiB
Python
import re
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import matplotlib.pyplot as plt
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h1 = {
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"loss": [],
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"c_acc": [],
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"top1": [],
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"top3": [],
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"top5": [],
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"val_loss": [],
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"val_top1": [],
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"val_top3": [],
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"val_top5": [],
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}
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h2 = {
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"loss": [],
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"c_acc": [],
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"top1": [],
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"top3": [],
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"top5": [],
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"val_loss": [],
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"val_top1": [],
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"val_top3": [],
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"val_top5": [],
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}
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h3 = {
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"loss": [],
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"c_acc": [],
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"top1": [],
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"top3": [],
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"top5": [],
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"val_loss": [],
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"val_top1": [],
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"val_top3": [],
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"val_top5": [],
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}
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d1 = {
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"train": {
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"top1": [
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0.25,
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0.3828125,
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0.515625,
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0.6015625,
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0.5625,
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0.6875,
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0.734375,
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0.7734375,
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0.6875,
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0.8046875,
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0.765625,
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0.8046875,
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0.84375,
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0.796875,
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0.8203125,
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0.84375,
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0.8203125,
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0.7734375,
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0.90625,
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0.8046875,
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0.8203125,
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0.890625,
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0.8515625,
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0.921875,
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0.9296875,
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0.875,
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0.875,
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0.8828125,
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0.90625,
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0.921875,
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0.875,
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0.8515625,
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0.875,
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0.890625,
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0.8828125,
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0.921875,
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0.8984375,
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0.921875,
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0.8671875,
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0.921875,
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0.8984375,
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0.9140625,
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0.8671875,
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0.8828125,
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0.9296875,
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0.84375,
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0.9296875,
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0.9296875,
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0.9140625,
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0.921875,
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0.921875,
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0.9296875,
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0.8984375,
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0.9375,
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0.9453125,
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0.8984375,
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0.96875,
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0.96875,
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0.921875,
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0.9609375,
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0.90625,
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0.953125,
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0.9453125,
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0.90625,
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0.9453125,
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0.9765625,
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0.96875,
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0.90625,
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0.9453125,
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0.9453125,
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0.921875,
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0.984375,
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0.953125,
|
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0.9375,
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|
0.921875,
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|
0.8984375,
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|
0.9765625,
|
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0.921875,
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0.9765625,
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0.953125,
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0.953125,
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0.9609375,
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0.9609375,
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0.9453125,
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0.96875,
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0.8984375,
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0.953125,
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0.953125,
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0.953125,
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0.890625,
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0.90625,
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0.9609375,
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0.9453125,
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0.9609375,
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0.8828125,
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0.9921875,
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0.9296875,
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0.9609375,
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0.9453125,
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0.9765625,
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],
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"top3": [
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0.5078125,
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0.625,
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0.7421875,
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0.890625,
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0.7578125,
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0.859375,
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0.8828125,
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0.96875,
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0.8984375,
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0.921875,
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0.9140625,
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0.9296875,
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0.9296875,
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0.953125,
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0.9140625,
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0.953125,
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0.9453125,
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0.9453125,
|
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0.984375,
|
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0.9296875,
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0.953125,
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0.953125,
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0.984375,
|
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0.96875,
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0.9765625,
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0.9609375,
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0.9765625,
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0.96875,
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0.984375,
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0.9609375,
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0.9765625,
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0.96875,
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0.9296875,
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0.96875,
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1.0,
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0.9765625,
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0.984375,
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0.984375,
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0.9296875,
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0.9921875,
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0.9765625,
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0.96875,
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0.9921875,
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0.9765625,
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0.96875,
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0.9609375,
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0.9921875,
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0.984375,
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0.96875,
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1.0,
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0.9921875,
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0.96875,
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0.9453125,
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0.9921875,
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1.0,
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0.9765625,
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1.0,
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1.0,
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0.984375,
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0.9921875,
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0.9921875,
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0.9921875,
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0.9921875,
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0.96875,
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0.9921875,
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0.9921875,
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1.0,
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0.9921875,
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0.9921875,
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0.9921875,
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0.984375,
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1.0,
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0.9921875,
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0.984375,
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0.9921875,
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0.984375,
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1.0,
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0.9921875,
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1.0,
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0.9765625,
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0.9921875,
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0.9921875,
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0.9921875,
|
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0.9765625,
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0.9765625,
|
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0.96875,
|
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0.9921875,
|
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1.0,
|
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0.984375,
|
|
0.9921875,
|
|
0.984375,
|
|
0.984375,
|
|
0.9921875,
|
|
0.9921875,
|
|
0.9921875,
|
|
0.9921875,
|
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0.9921875,
|
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0.9921875,
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0.9765625,
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1.0,
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],
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},
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"val": {
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"top1": [
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0.1616666615009308,
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0.16944444179534912,
|
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0.1688888818025589,
|
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0.1666666716337204,
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0.16388888657093048,
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0.17777778208255768,
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0.18222221732139587,
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0.18222221732139587,
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0.17777778208255768,
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0.1738888919353485,
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0.1711111068725586,
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0.19499999284744263,
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0.18166667222976685,
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0.1827777773141861,
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0.17944444715976715,
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0.18777777254581451,
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0.18444444239139557,
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0.18222221732139587,
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0.18444444239139557,
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0.18888889253139496,
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0.19833333790302277,
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0.19722221791744232,
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0.18888889253139496,
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0.19611111283302307,
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0.1922222226858139,
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0.1899999976158142,
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0.19499999284744263,
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0.21111111342906952,
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0.20222222805023193,
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0.19555555284023285,
|
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0.20333333313465118,
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0.1944444477558136,
|
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0.18666666746139526,
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0.18388888239860535,
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0.18722222745418549,
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0.19277778267860413,
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0.18888889253139496,
|
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0.212777778506279,
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0.20666666328907013,
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0.20777778327465057,
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0.19277778267860413,
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0.19277778267860413,
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0.19611111283302307,
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0.19277778267860413,
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0.21166667342185974,
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0.20555555820465088,
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0.20555555820465088,
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0.2105555534362793,
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0.20444443821907043,
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0.20499999821186066,
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0.2061111181974411,
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0.21222221851348877,
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0.2011111080646515,
|
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0.19499999284744263,
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0.2083333283662796,
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0.1988888829946518,
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0.2038888931274414,
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0.20777778327465057,
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0.212777778506279,
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0.22166666388511658,
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0.21666666865348816,
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0.22055555880069733,
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0.20000000298023224,
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0.2177777737379074,
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0.2222222238779068,
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0.22333332896232605,
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0.2222222238779068,
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0.22277778387069702,
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0.21833333373069763,
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0.20722222328186035,
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0.21833333373069763,
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0.2150000035762787,
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0.2133333384990692,
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0.21722222864627838,
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0.21888889372348785,
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0.20999999344348907,
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0.2244444489479065,
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0.21166667342185974,
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0.21166667342185974,
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0.2177777737379074,
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0.2266666740179062,
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0.21166667342185974,
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0.22555555403232574,
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0.19777777791023254,
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|
0.23111110925674438,
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0.20666666328907013,
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0.21222221851348877,
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0.21555554866790771,
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0.20555555820465088,
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0.212777778506279,
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0.21611110866069794,
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0.20777778327465057,
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0.212777778506279,
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0.21444444358348846,
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0.2199999988079071,
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0.21944443881511688,
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|
0.2199999988079071,
|
|
0.2061111181974411,
|
|
0.20999999344348907,
|
|
0.2222222238779068,
|
|
],
|
|
"top3": [
|
|
0.38777777552604675,
|
|
0.36944442987442017,
|
|
0.3861111104488373,
|
|
0.3916666805744171,
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|
0.3766666650772095,
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0.3977777659893036,
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|
0.38777777552604675,
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0.39888888597488403,
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0.39888888597488403,
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0.40833333134651184,
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0.398333340883255,
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0.40666666626930237,
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0.40611112117767334,
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0.41111111640930176,
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0.41499999165534973,
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0.41333332657814026,
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0.426111102104187,
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0.4033333361148834,
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0.4194444417953491,
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0.4194444417953491,
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0.43666666746139526,
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0.4211111068725586,
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0.4072222113609314,
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0.4216666519641876,
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0.42722222208976746,
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0.41777777671813965,
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0.4233333468437195,
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0.43166667222976685,
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0.43833333253860474,
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0.4327777922153473,
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0.4238888919353485,
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0.41499999165534973,
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0.4033333361148834,
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0.4211111068725586,
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0.4161111116409302,
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0.41055554151535034,
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0.43111109733581543,
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0.43444445729255676,
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0.4283333420753479,
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0.44388890266418457,
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0.4216666519641876,
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0.42444443702697754,
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0.4216666519641876,
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0.4161111116409302,
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0.43833333253860474,
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0.4211111068725586,
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0.4238888919353485,
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0.4305555522441864,
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0.4233333468437195,
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0.4327777922153473,
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0.4305555522441864,
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0.44777777791023254,
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0.4211111068725586,
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0.43833333253860474,
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0.43833333253860474,
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0.43833333253860474,
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0.43444445729255676,
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0.43111109733581543,
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0.44611111283302307,
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0.44999998807907104,
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0.4483333230018616,
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0.4427777826786041,
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0.4355555474758148,
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0.44333332777023315,
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0.4399999976158142,
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0.44611111283302307,
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0.4427777826786041,
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0.43888887763023376,
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0.4394444525241852,
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0.43833333253860474,
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0.4449999928474426,
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0.45722222328186035,
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0.4422222077846527,
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0.44555556774139404,
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0.4538888931274414,
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0.4444444477558136,
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0.46222221851348877,
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0.4350000023841858,
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0.4427777826786041,
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0.46888887882232666,
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0.4605555534362793,
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0.4627777636051178,
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0.4211111068725586,
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0.4577777683734894,
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0.4588888883590698,
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0.45055556297302246,
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0.45444443821907043,
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0.4516666531562805,
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0.44999998807907104,
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0.43888887763023376,
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0.44555556774139404,
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0.4466666579246521,
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0.43833333253860474,
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0.4444444477558136,
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0.4555555582046509,
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0.4605555534362793,
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0.4633333384990692,
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0.43666666746139526,
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0.45055556297302246,
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],
|
|
},
|
|
}
|
|
d2 = {
|
|
"train": {
|
|
"top1": [
|
|
0.3125,
|
|
0.5703125,
|
|
0.6875,
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0.7890625,
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0.8203125,
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0.8125,
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0.859375,
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0.921875,
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0.890625,
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0.8828125,
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0.8828125,
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0.8828125,
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0.859375,
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0.890625,
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0.875,
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0.8671875,
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0.890625,
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0.9140625,
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0.921875,
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0.8671875,
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0.9296875,
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0.9453125,
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0.921875,
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0.9453125,
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0.9453125,
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0.9453125,
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0.96875,
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0.9296875,
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0.9140625,
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0.90625,
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0.9765625,
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0.9296875,
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0.8984375,
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0.890625,
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0.9453125,
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0.8984375,
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0.953125,
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0.9140625,
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0.9375,
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0.90625,
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0.921875,
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0.9296875,
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0.9375,
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0.953125,
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0.9296875,
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0.9453125,
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0.9453125,
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0.953125,
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0.96875,
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0.921875,
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0.96875,
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0.96875,
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0.9921875,
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0.9765625,
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0.9921875,
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0.9609375,
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0.9609375,
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0.9765625,
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0.96875,
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0.9609375,
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0.9609375,
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0.984375,
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0.9765625,
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0.984375,
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0.95703125,
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0.97265625,
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0.96875,
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0.94921875,
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0.98046875,
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0.97265625,
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0.9765625,
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0.98046875,
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0.953125,
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0.97265625,
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0.9453125,
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0.97265625,
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0.953125,
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0.9453125,
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0.9609375,
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0.94921875,
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0.9765625,
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0.96875,
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0.24611110985279083,
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0.25055554509162903,
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0.25833332538604736,
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0.25611111521720886,
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],
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"top3": [
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0.4186111092567444,
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0.449444442987442,
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0.4577777683734894,
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0.45444443821907043,
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0.47333332896232605,
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0.48500001430511475,
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],
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},
|
|
}
|
|
|
|
with open("/home/laurent/Documents/Cours/ENSEEIHT/S9 - IAM/Projet/src/recup_baseline.txt", "r") as file:
|
|
for line in file:
|
|
matches = re.finditer(r"(val_loss|loss): ([0-9.]+)", line)
|
|
for match in matches:
|
|
metric_name = match.group(1)
|
|
metric_value = float(match.group(2))
|
|
h1[metric_name].append(metric_value)
|
|
|
|
# with open("/home/laurent/Documents/Cours/ENSEEIHT/S9 - IAM/Projet/src/recup_semisup.txt", "r") as file:
|
|
# for line in file:
|
|
# matches = re.finditer(r"(val_top1|val_top3): ([0-9.]+)", line)
|
|
# for match in matches:
|
|
# metric_name = match.group(1)
|
|
# metric_value = float(match.group(2))
|
|
# h2[metric_name].append(metric_value)
|
|
|
|
# with open("/home/laurent/Documents/Cours/ENSEEIHT/S9 - IAM/Projet/src/recup_finetuning.txt", "r") as file:
|
|
# for line in file:
|
|
# matches = re.finditer(r"(val_top1|val_top3): ([0-9.]+)", line)
|
|
# for match in matches:
|
|
# metric_name = match.group(1)
|
|
# metric_value = float(match.group(2))
|
|
# h3[metric_name].append(metric_value)
|
|
|
|
# plt.figure(figsize=(15, 10))
|
|
# plt.plot(d1["val"]["top1"], label="baseline_val_top1")
|
|
# # plt.plot(h1["val_top3"], color="green", label="baseline_top3")
|
|
# plt.plot(d2["val"]["top1"], label="semisup_val_top1")
|
|
# # plt.plot(h2["val_top3"], color="green", label="semisup_top3")
|
|
# plt.plot(d3["val"]["top1"], label="pretrain_val_top1")
|
|
# # plt.plot(h3["val_top3"], color="green", label="finetuning_top3")
|
|
# plt.xlabel("Epochs")
|
|
# plt.ylabel("Accuracy")
|
|
# plt.legend()
|
|
# plt.grid()
|
|
# plt.savefig("comp_accuracy_sgan.png", dpi=300, transparent=True, bbox_inches="tight", pad_inches=0)
|
|
|
|
plt.figure(figsize=(15, 10))
|
|
plt.plot(h1["loss"], color="purple", label="loss")
|
|
plt.plot(h1["val_loss"], color="purple", label="val_loss", linestyle="--")
|
|
plt.ylabel("Loss")
|
|
plt.xlabel("Epochs")
|
|
plt.legend()
|
|
plt.grid()
|
|
plt.savefig("baseline_loss_simclr.png", dpi=300, transparent=True, bbox_inches="tight", pad_inches=0)
|