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Author SHA1 Message Date
Elaina Claus
bf15e7b6c8 save the plots for viewing 2025-12-18 22:24:23 -05:00
Elaina Claus
0385b0acc8 testing adadelta optimizer 2025-12-18 22:24:01 -05:00
3 changed files with 257 additions and 542 deletions
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1
.gitignore vendored
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@@ -1,3 +1,4 @@
.venv/
*.png
*.pth
*.log

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output_graphs.py
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@@ -3,544 +3,255 @@ import pandas as pd
import matplotlib.pyplot as plt
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INFO:__main__:step=11400 loss=0.0002022 acc=1.0000000
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INFO:__main__:step=12200 loss=0.0001641 acc=1.0000000
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INFO:__main__:step=12400 loss=0.0002605 acc=1.0000000
INFO:__main__:step=12500 loss=0.0001344 acc=1.0000000
INFO:__main__:step=12600 loss=0.0002226 acc=0.9999390
INFO:__main__:step=12700 loss=0.0002189 acc=0.9999390
INFO:__main__:step=12800 loss=0.0001012 acc=1.0000000
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INFO:__main__:step=13100 loss=0.0001643 acc=1.0000000
INFO:__main__:step=13200 loss=0.0001547 acc=0.9999390
INFO:__main__:step=13300 loss=0.0002164 acc=1.0000000
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INFO:__main__:step=13500 loss=0.0001582 acc=1.0000000
INFO:__main__:step=13600 loss=0.0002629 acc=0.9999390
INFO:__main__:step=13700 loss=0.0002293 acc=1.0000000
INFO:__main__:step=13800 loss=0.0001947 acc=1.0000000
INFO:__main__:step=13900 loss=0.0001451 acc=1.0000000
INFO:__main__:step=14000 loss=0.0002371 acc=1.0000000
INFO:__main__:step=14100 loss=0.0003281 acc=1.0000000
INFO:__main__:step=14200 loss=0.0002205 acc=1.0000000
INFO:__main__:step=14300 loss=0.0001904 acc=1.0000000
INFO:__main__:step=14400 loss=0.0001126 acc=1.0000000
INFO:__main__:step=14500 loss=0.0002144 acc=1.0000000
INFO:__main__:step=14600 loss=0.0001922 acc=1.0000000
INFO:__main__:step=14700 loss=0.0002118 acc=1.0000000
INFO:__main__:step=14800 loss=0.0001527 acc=1.0000000
INFO:__main__:step=14900 loss=0.0000752 acc=1.0000000
INFO:__main__:step=15000 loss=0.0002345 acc=1.0000000
INFO:__main__:step=15100 loss=0.0002119 acc=1.0000000
INFO:__main__:step=15200 loss=0.0001223 acc=1.0000000
INFO:__main__:step=15300 loss=0.0000772 acc=1.0000000
INFO:__main__:step=15400 loss=0.0001805 acc=0.9999390
INFO:__main__:step=15500 loss=0.0003057 acc=1.0000000
INFO:__main__:step=15600 loss=0.0002293 acc=1.0000000
INFO:__main__:step=15700 loss=0.0000739 acc=1.0000000
INFO:__main__:step=15800 loss=0.0001586 acc=1.0000000
INFO:__main__:step=15900 loss=0.0001513 acc=1.0000000
INFO:__main__:step=16000 loss=0.0001348 acc=1.0000000
INFO:__main__:step=16100 loss=0.0002099 acc=1.0000000
INFO:__main__:step=16200 loss=0.0001405 acc=1.0000000
INFO:__main__:step=16300 loss=0.0003015 acc=0.9998779
INFO:__main__:step=16400 loss=0.0000603 acc=1.0000000
INFO:__main__:step=16500 loss=0.0001273 acc=1.0000000
INFO:__main__:step=16600 loss=0.0001151 acc=1.0000000
INFO:__main__:step=16700 loss=0.0001440 acc=1.0000000
INFO:__main__:step=16800 loss=0.0002359 acc=1.0000000
INFO:__main__:step=16900 loss=0.0002146 acc=1.0000000
INFO:__main__:step=17000 loss=0.0002382 acc=1.0000000
INFO:__main__:step=17100 loss=0.0000885 acc=1.0000000
INFO:__main__:step=17200 loss=0.0002271 acc=0.9999390
INFO:__main__:step=17300 loss=0.0000785 acc=1.0000000
INFO:__main__:step=17400 loss=0.0002242 acc=1.0000000
INFO:__main__:step=17500 loss=0.0001646 acc=1.0000000
INFO:__main__:step=17600 loss=0.0001174 acc=1.0000000
INFO:__main__:step=17700 loss=0.0001843 acc=0.9999390
INFO:__main__:step=17800 loss=0.0001872 acc=1.0000000
INFO:__main__:step=17900 loss=0.0001122 acc=1.0000000
INFO:__main__:step=18000 loss=0.0000516 acc=1.0000000
INFO:__main__:step=18100 loss=0.0001427 acc=1.0000000
INFO:__main__:step=18200 loss=0.0000453 acc=1.0000000
INFO:__main__:step=18300 loss=0.0001730 acc=1.0000000
INFO:__main__:step=18400 loss=0.0001801 acc=1.0000000
INFO:__main__:step=18500 loss=0.0001219 acc=1.0000000
INFO:__main__:step=18600 loss=0.0001443 acc=1.0000000
INFO:__main__:step=18700 loss=0.0003240 acc=1.0000000
INFO:__main__:step=18800 loss=0.0001341 acc=1.0000000
INFO:__main__:step=18900 loss=0.0000698 acc=1.0000000
INFO:__main__:step=19000 loss=0.0002490 acc=1.0000000
INFO:__main__:step=19100 loss=0.0002027 acc=1.0000000
INFO:__main__:step=19200 loss=0.0001338 acc=0.9999390
INFO:__main__:step=19300 loss=0.0001596 acc=1.0000000
INFO:__main__:step=19400 loss=0.0001416 acc=0.9999390
INFO:__main__:step=19500 loss=0.0001592 acc=1.0000000
INFO:__main__:step=19600 loss=0.0002262 acc=1.0000000
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INFO:__main__:step=19800 loss=0.0001256 acc=1.0000000
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INFO:__main__:step=20000 loss=0.0000979 acc=1.0000000
INFO:__main__:step=20100 loss=0.0002766 acc=1.0000000
INFO:__main__:step=20200 loss=0.0003364 acc=0.9999390
INFO:__main__:step=20300 loss=0.0001628 acc=1.0000000
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INFO:__main__:step=20600 loss=0.0001439 acc=1.0000000
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INFO:__main__:step=20900 loss=0.0000641 acc=1.0000000
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INFO:__main__:step=21100 loss=0.0002183 acc=1.0000000
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INFO:__main__:step=21300 loss=0.0001134 acc=1.0000000
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INFO:__main__:step=21500 loss=0.0001587 acc=1.0000000
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INFO:__main__:step=21800 loss=0.0001406 acc=1.0000000
INFO:__main__:step=21900 loss=0.0001177 acc=1.0000000
INFO:__main__:step=22000 loss=0.0000872 acc=1.0000000
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INFO:__main__:step=22200 loss=0.0000653 acc=1.0000000
INFO:__main__:step=22300 loss=0.0001202 acc=1.0000000
INFO:__main__:step=22400 loss=0.0002056 acc=1.0000000
INFO:__main__:step=22500 loss=0.0001006 acc=1.0000000
INFO:__main__:step=22600 loss=0.0001436 acc=1.0000000
INFO:__main__:step=22700 loss=0.0001289 acc=1.0000000
INFO:__main__:step=22800 loss=0.0000839 acc=1.0000000
INFO:__main__:step=22900 loss=0.0001841 acc=1.0000000
INFO:__main__:step=23000 loss=0.0000884 acc=1.0000000
INFO:__main__:step=23100 loss=0.0000641 acc=1.0000000
INFO:__main__:step=23200 loss=0.0001370 acc=1.0000000
INFO:__main__:step=23300 loss=0.0002339 acc=0.9998779
INFO:__main__:step=23400 loss=0.0001042 acc=1.0000000
INFO:__main__:step=23500 loss=0.0001897 acc=1.0000000
INFO:__main__:step=23600 loss=0.0001677 acc=1.0000000
INFO:__main__:step=23700 loss=0.0001252 acc=1.0000000
INFO:__main__:step=23800 loss=0.0001060 acc=1.0000000
INFO:__main__:step=23900 loss=0.0001251 acc=1.0000000
INFO:__main__:step=24000 loss=0.0001638 acc=1.0000000
INFO:__main__:step=24100 loss=0.0001202 acc=1.0000000
INFO:__main__:step=24200 loss=0.0001683 acc=1.0000000
INFO:__main__:step=24300 loss=0.0000737 acc=1.0000000
INFO:__main__:step=24400 loss=0.0001864 acc=1.0000000
INFO:__main__:step=24500 loss=0.0001836 acc=1.0000000
INFO:__main__:step=24600 loss=0.0001927 acc=1.0000000
INFO:__main__:step=24700 loss=0.0000401 acc=1.0000000
INFO:__main__:step=24800 loss=0.0002011 acc=1.0000000
INFO:__main__:step=24900 loss=0.0002700 acc=0.9998779
"""
pattern = re.compile(r"step=\s*(\d+)\s+loss=([0-9.]+)\s+acc=([0-9.]+)")
@@ -559,6 +270,7 @@ plt.xlabel("Step")
plt.ylabel("Loss (log scale)")
plt.title("Training Loss vs Step")
plt.tight_layout()
plt.savefig('training_loss_v_step.png')
plt.show()
# Plot 2: Accuracy
@@ -570,4 +282,5 @@ plt.xlabel("Step")
plt.ylabel("Error rate (1 - accuracy) (log scale)")
plt.title("Training Error Rate vs Step")
plt.tight_layout()
plt.savefig('training_error_v_step.png')
plt.show()

9
pairwise_compare.py
View File

@@ -11,12 +11,12 @@ DEVICE = torch.accelerator.current_accelerator() if torch.accelerator.is_availab
# Valves
DIMENSIONS = 1
TRAIN_STEPS = 20000
TRAIN_STEPS = 25000
TRAIN_BATCHSZ = 16384
TRAIN_PROGRESS = 500
TRAIN_PROGRESS = 100
BATCH_LOWER = -512.0
BATCH_UPPER = 512.0
DO_VERBOSE_EARLY_TRAIN = True
DO_VERBOSE_EARLY_TRAIN = False
def get_torch_info():
log.info("PyTorch Version: %s", torch.__version__)
@@ -86,7 +86,8 @@ def training_entry():
set_seed(0)
model = PairwiseComparator(d=DIMENSIONS).to(DEVICE)
opt = torch.optim.AdamW(model.parameters(), lr=2e-3)
# opt = torch.optim.AdamW(model.parameters(), lr=2e-3)
opt = torch.optim.Adadelta(model.parameters(), lr=1.0)
# 4) Train
for step in range(TRAIN_STEPS):