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Updated README and better epoch-printing

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Dominik Moritz Roth 2021-09-22 10:28:08 +02:00
parent 5f0f9f1dce
commit 1b4b6f1a2f
2 changed files with 4 additions and 2 deletions
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1
README.md
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@ -6,3 +6,4 @@ I made it because I want to try to break it.
This will work iff I succeed in building a PPT-discriminator for sha256 from randomness
As my first approach this discriminator will be based on an LSTM-network.
Update: This worked out way better than expected; given long enought sequences (128 Bytes are more than enough) we can discriminate successfully in 100% of cases.
Update: I did an upsie in the training-code and the discriminator is actually shit.

5
train.py
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@ -4,6 +4,7 @@ from torch import nn, optim
from torch.utils.data import DataLoader
import numpy as np
import random
import math
import shark
@ -34,7 +35,7 @@ def train(model, seq_len=16*64):
tid = str(int(random.random()*99999)).zfill(5)
print("[i] I am "+str(tid))
ltLoss = 50
lltLoss = 51
lltLoss = 52
model.train()
criterion = nn.BCELoss()
@ -67,7 +68,7 @@ def train(model, seq_len=16*64):
correct[t] = round(y_pred.item()) == t
ltLoss = ltLoss*0.9 + 0.1*loss.item()
lltLoss = lltLoss*0.9 + 0.1*ltLoss
print({ 'epoch': epoch, 'loss': loss.item(), 'ltLoss': ltLoss, 'ok0': correct[0], 'ok1': correct[1], 'succ': correct[0] and correct[1] })
print({ 'epoch': epoch, 'loss': loss.item(), 'ltLoss': ltLoss, 'ok0': correct[0], 'ok1': correct[1], 'succ': correct[0] and correct[1], 'acc': str(int(max(0, 1-math.sqrt(lltLoss))*100))+"%" })
if epoch % 8 == 0:
torch.save(model.state_dict(), 'model_savepoints/'+tid+'_'+str(epoch)+'.n')