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In machine learning, logits refer to the raw, unscaled output values from the final layer of a neural network—before any activation function like softmax or sigmoid is applied. They are not probabilities yet, but they play a critical role in converting model predictions into understandable outputs.
Logits are:
While it’s tempting to work directly with probabilities, training neural networks using logits offers numerical stability and avoids problems like:
For this reason, popular loss functions like CrossEntropyLoss (for multi-class classification) and BCEWithLogitsLoss (for binary classification) expect logits, not probabilities.
Use Case | Details |
---|---|
Binary Classification | Output one logit, then apply sigmoid for probability. |
Multi-Class Classification | Output multiple logits, then apply softmax to interpret class probs. |
Loss Calculation | Use logits directly with loss functions for better accuracy and speed. |
Let’s say you’re building a spam classifier:
python
logit = 1.2 # Raw output from final layer
python
from math import exp
prob = 1 / (1 + exp(-logit)) # ≈ 0.768
print(f"Spam probability: {prob:.2%}")
This output means the model is 76.8% confident the email is spam.
python
import torch
import torch.nn as nn
loss_fn = nn.BCEWithLogitsLoss()
output = torch.tensor([logit])
target = torch.tensor([1.0]) # True label = spam
loss = loss_fn(output, target)
print(loss.item())
Suppose your model predicts whether an image is a Cat, Dog, or Rabbit:
python
import torch
logits = torch.tensor([2.5, 0.3, -1.2]) # Cat, Dog, Rabbit
python
import torch.nn.functional as F
probs = F.softmax(logits, dim=0)
print(probs) # tensor([0.81, 0.13, 0.06])
python
loss_fn = torch.nn.CrossEntropyLoss()
labels = torch.tensor([0]) # True class is 'Cat'
loss = loss_fn(logits.unsqueeze(0), labels)
print(loss.item())
From classification to prediction, we help you implement logits in machine learning models for accurate, stable, and scalable results. Let’s transform your data into insights.
Understanding and leveraging logits correctly can make or break your model’s training effectiveness and prediction accuracy.
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