What is the Meaning of the Word “Logits” in TensorFlow?

TensorFlow features logits as the unprocessed scores obtained from neural network outputs that need activation functions like softmax or sigmoid before normalization. The concept of logits has likely appeared to you at least once if you worked with TensorFlow in deep learning applications. Does the usage of logits produce any clear definition? Why do we select logits over using probabilities? Are logits the raw outputs before any activation function when applied to neural networks?

In this blog, we’ll break down the concept of logits in a simple way so that it’s easy for you to understand. So let’s get started!

Table of Contents

• What are Logits in TensorFlow?
• Why Do We Use Logits Instead of Probabilities?
• How to Work with Logits in TensorFlow?
• How to Convert Logits to Probabilities?
• Logits vs. Probabilities: Key Differences
• Relationship Between Logits and Loss Functions
• How Logits Affect Gradient Descent & Model Training?
• Logits in Different Machine Learning Architectures
• Logits and Temperature Scaling for Model Calibration
• Conclusion

What are Logits in TensorFlow?

The last neural network layer in TensorFlow generates logits, which represent unnormalized scores that are provided before applying activation functions such as softmax or sigmoid.

These logits are then used to calculate probabilities using activation functions.

                                            Logits = W . X + b

Where:

• W = Weights of the model
• X = Input Features
• b = Bias

Example:

Imagine a model that can predict a dog, cat, and bird from an image.

It might produce the following logits as outputs (raw scores):

After the application of the softmax function, these logits are converted into probabilities.

As you can see above, logits don’t sum to 1, but probabilities do!

Why Do We Use Logits Instead of Probabilities?

The decision to use logits instead of probabilities remains puzzling to some because it would seem more logical to generate direct probability outputs.

Given below are the points that mention why logits are better than probabilities:

It brings More Numerical Stability

• Softmax performs exponentiation, yet this operation leads to floating-point instability before the final steps of the process.
• The logits method eliminates potential NaN errors that occur due to small or large value generation in training processes.

It is easier to Compute Loss Functions

• The categorical cross-entropy loss performs most efficiently when provided with logits instead of predicted probabilities.
• The tf.nn.softmax_cross_entropy_with_logits() function in TensorFlow receives logits directly to produce more efficient performance.

It Allows Negative Values

• Probabilities must be between 0 and 1.
• Logits can be positive or negative, which makes them more flexible before final activation.

How to Work with Logits in TensorFlow?

Let’s see how TensorFlow handles logits in a deep learning model.

Using Logits in a Neural Network

Here’s an example of a classification model using TensorFlow/Keras:

Example:

Output:

Explanation:

• The last layer functions without an activation since it directly provides output digits.
• The TensorFlow loss function SparseCategoricalCrossentropy(from_logits=True) indicates that raw logits should be used so it handles the softmax application internally.

How to Convert Logits to Probabilities?

Given below are the two methods that are used to convert Logits to Probabilities.

Method 1: Softmax for Multi-Class Classification

Example:

Output:

Explanation:

The Softmax function outputs probabilities with their total sum at 1, according to the previous example.

Method 2: Sigmoid for Binary Classification

If the output has only one neuron, we use sigmoid to get probabilities between 0 and 1.

Example:

Output:

From the above output, we can see that Sigmoid converts logits into probabilities for binary classification.

Logits vs. Probabilities: Key Differences

The table given below shows the difference between Logits and Probabilities.

Logits are used during training, while probabilities are used for final predictions.

Relationship Between Logits and Loss Functions

1. Why do Loss Functions Use Logits Instead of Probabilities?

The primary function of logits pertains to their application in loss functions. In TensorFlow, most loss functions expect arguments in the format of logits, including SparseCategoricalCrossentropy and BinaryCrossentropy. Logits enable better numerical stability because they stop floating-point precision errors from occurring.

For example, TensorFlow provides a specialized loss function.

Explanation:

The from_logits argument enables TensorFlow to apply softmax internally before computing the loss because the model provides logits as its final outputs.

2. Why is this important?

• The manual application of the softmax operation before the loss function produces gradient instability.
• Direct usage of logits eliminates precision errors that would occur while calculating backpropagation gradients.

How Logits Affect Gradient Descent & Model Training?

 How Do Logits Impact Back Propagation?

During backpropagation, the calculation of gradients depends on logits. The computation of gradients by loss functions depends on logits to determine their effects in the following ways:

• The scale of gradient updates
• The stability of training
• How quickly/slowly a model converges

Example: If logits are extremely large before softmax (e.g., [1000, 2000, 3000] ), the softmax function will end up in exploding gradients. On the other hand, if logits are too small ( [-10, -20, -30] ), the gradients may vanish, leading to slow learning.

Solution: TensorFlow often normalizes logits internally to prevent gradient issues.

Logits in Different Machine Learning Architectures

How are Logits Used in NLP, Computer Vision, and Reinforcement Learning?

The concept of logits serves different purposes in three main AI fields where NLP meets computer vision meets reinforcement learning. Multiple utilization methods of logits appear in the following list:

Method 1: Logits in NLP (Natural Language Processing)

• Transformers, including BERT and GPT, utilize logits for determining token probabilities right before applying softmax in text classification tasks.
• In sentiment analysis, the logits help forecast whether the content displays positive or negative sentiment.

Method 2: Logits in Computer Vision

• Real-valued logits serve as the input to object detection models such as YOLO and Faster R-CNN before their softmax operation.
• The network outputting logits gets transformed into probabilities through a conversion process to make predictions about dog, cat, or bird classification.

Method 3: Logits in Reinforcement Learning (RL)

• Policy-based reinforcement learning systems, including PPO or A2C, employ logits as a way to express unprocessed action preferences straight before performing an action selection.
• Before making its move selection in a game, a reinforcement learning agent uses logits as its input.

Logits and Temperature Scaling for Model Calibration

What is Temperature Scaling, and Why is it Important?

One common problem in deep learning is that models tend to be overconfident in their predictions. This happens because the softmax function can push probabilities too close to 1, even when the model is uncertain.

The solution to this issue is Temperature Scaling.

We can introduce a temperature parameter (T) to control the sharpness of the softmax distribution:

• Higher T (e.g., T=2): Makes probabilities smoother, reducing overconfidence.
• Lower T (e.g., T=0.5): Makes probabilities sharper, increasing confidence.

Example: Temperature Scaling in TensorFlow

Output:

Explanation:

Temperature scaling is widely used in model calibration for real-world applications like medical AI, finance, and self-driving cars.

Conclusion

Anyone who uses deep learning models with TensorFlow needs to master logits for their applications. The unnormalized predictions that serve as logits deliver better numerical stability to training performance while working with appropriate loss functions. Logs serve as building blocks for both explanation techniques and optimization in advanced techniques, but probabilities remain more naturally suited to understanding model predictions. Learning which logits to utilize with proper implementation allows you to create stronger, more accurate computational models throughout computer vision, NLP, and reinforcement learning domains. Learning how to properly handle logits will provide both beginners and practitioners of AI with an enhanced understanding of neural networks while helping them develop more accurate and reliable models.

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