What are Logits in TensorFlow?
In TensorFlow, “logits” represent the raw, unscaled output of a neural network’s final layer before applying an activation function. They essentially hold the pre-activation values for each class in a multi-class classification problem.
Why Use Logits?
- Efficiency: Computing logits directly from the network’s output avoids unnecessary computations, especially during training.
- Numerical Stability: Logits are more numerically stable than probabilities, especially when dealing with very small or very large values.
- Flexibility: Logits can be easily transformed into probabilities using various activation functions like sigmoid, softmax, or ReLU.
Logits vs. Probabilities
Logits and probabilities are closely related but represent different concepts:
| Concept | Description |
|---|---|
| Logits | Unscaled values representing the pre-activation scores for each class. |
| Probabilities | Scaled values between 0 and 1 representing the likelihood of each class. |
Example: Binary Classification
# Input data
x = tf.constant([[1.0, 2.0, 3.0]])
# Define a simple neural network
model = tf.keras.Sequential([
tf.keras.layers.Dense(4, activation='relu'),
tf.keras.layers.Dense(1)
])
# Get the logits
logits = model(x)
# Apply sigmoid activation to obtain probabilities
probabilities = tf.sigmoid(logits)
In this example, the output of the model (logits) is a tensor containing the raw scores. The sigmoid function converts these logits into probabilities ranging from 0 to 1.
Summary
Logits are an essential concept in TensorFlow, representing the pre-activation values of a neural network’s output. They provide numerical stability, efficiency, and flexibility in classification tasks. Understanding logits allows you to effectively work with neural networks and interpret their predictions.