write all data on training to training log
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@@ -176,18 +176,17 @@ def training_entry():
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loss_fn.backward()
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opt.step()
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if step % TRAIN_PROGRESS == 0:
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with torch.no_grad():
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pred = (torch.sigmoid(logits) > 0.5).float()
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acc = (pred == y).float().mean().item()
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tlog.write(f"step={step:5d} loss={loss_fn.item():.7f} acc={acc:.7f}\n")
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with torch.no_grad():
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pred = (torch.sigmoid(logits) > 0.5).float()
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acc = (pred == y).float().mean().item()
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tlog.write(f"step={step:5d} loss={loss_fn.item():.7f} acc={acc:.7f}\n")
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# also print to normal text log occasionally to show some activity.
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# every 100 steps check if its been longer than 5 seconds since we've updated the user
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if step % 100 == 0:
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if (datetime.datetime.now() - last_ack).total_seconds() > 5:
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log.info(f"still training... step={step} of {TRAIN_STEPS}")
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last_ack = datetime.datetime.now()
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# also print to normal text log occasionally to show some activity.
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# every 100 steps check if its been longer than 5 seconds since we've updated the user
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if step % 100 == 0:
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if (datetime.datetime.now() - last_ack).total_seconds() > 5:
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log.info(f"still training... step={step} of {TRAIN_STEPS}")
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last_ack = datetime.datetime.now()
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training_end_time = datetime.datetime.now()
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log.info(f"Training steps complete. Start time: {training_start_time} End time: {training_end_time}")
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