When working with machine learning models, especially in classification tasks, an essential part of the process is determining the loss function. In TensorFlow/Keras, one commonly used loss function for classification tasks is `sparse_categorical_crossentropy`. This loss function is pivotal in scenarios where you want to allocate probabilistic scores to different classes. A particular parameter within this function is `from_logits`, which may appear ambiguous to newcomers but plays a crucial role in determining how the input predictions are interpreted. Let's delve into what `from_logits = True or False` means within the context of `sparse_categorical_crossentropy`.

Technical Explanation

In machine learning, model outputs often represent probabilities. For instance, in classification tasks with multiple classes, the output layer often includes a softmax activation function that normalizes model scores (or logits) into probabilities. This is where the concept of logits versus probabilities arises, and consequently, the relevance of the `from_logits` argument.

Logits vs Probabilities

• Logits: These are raw, non-normalized scores outputted by the final layer of a neural network. They can take any real values and have not been passed through a softmax function.
• Probabilities: These are values that have been normalized (usually using a softmax function) to sum to 1, making them interpretable as probabilities.

Using `from_logits` in `sparse_categorical_crossentropy`

`from_logits=False`

When `from_logits=False`, TensorFlow assumes that the model outputs are probabilities. In this case, the output layer of your model should include a softmax activation function. The role of softmax is to convert logits to probabilities, ensuring that the sum of the probabilities is 1 across all classes. Here’s an example:

• Numerical Stability: Directly working with logits and letting sparse_categorical_crossentropy handle softmax can offer numerical stability benefits, particularly with large or very small output values.
• Performance: Avoiding softmax in the model layer reduces overhead when dealing with large-scale neural networks, where executing softmax on the GPU can slow down computations.