The softmax function highlights the largest values and suppresses values which are significantly below the maximum value, though this is not true for small values. It normalizes the outputs so that they sum to 1 so that they can be directly treated as probabilities over the output.
It is often used in the final layer in a classifier model with the categorical crossentropy as loss function.
Stay in the know by signing up for occasional emails with tips, tricks, deep learning insights, product updates, event news and webinar invitations.
We promise not to spam you or share your email with any third party. You can change your preferences at any time. See our privacy policies.
Please check your email inbox account to confirm, set, or update your communication preferences.