Translations:Softmax Function/30/en: Difference between revisions

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    # A neural network produces raw {{Term|logits}} <math>\mathbf{z}</math> from its final linear layer.
    # A neural network produces raw logits <math>\mathbf{z}</math> from its final linear layer.
    # Softmax converts {{Term|logits}} to probabilities: <math>\hat{\mathbf{y}} = \sigma(\mathbf{z})</math>.
    # Softmax converts logits to probabilities: <math>\hat{\mathbf{y}} = \sigma(\mathbf{z})</math>.
    # The predicted class is <math>\hat{c} = \arg\max_k \hat{y}_k</math>.
    # The predicted class is <math>\hat{c} = \arg\max_k \hat{y}_k</math>.
    # Training uses [[Cross-Entropy Loss]] applied to the predicted distribution and the true labels.
    # Training uses [[Cross-Entropy Loss]] applied to the predicted distribution and the true labels.

    Revision as of 22:02, 27 April 2026

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    Message definition (Softmax Function)
    # A neural network produces raw {{Term|logits}} <math>\mathbf{z}</math> from its final linear layer.
    # Softmax converts {{Term|logits}} to probabilities: <math>\hat{\mathbf{y}} = \sigma(\mathbf{z})</math>.
    # The predicted class is <math>\hat{c} = \arg\max_k \hat{y}_k</math>.
    # Training uses [[Cross-Entropy Loss]] applied to the predicted distribution and the true labels.
    1. A neural network produces raw logits $ \mathbf{z} $ from its final linear layer.
    2. Softmax converts logits to probabilities: $ \hat{\mathbf{y}} = \sigma(\mathbf{z}) $.
    3. The predicted class is $ \hat{c} = \arg\max_k \hat{y}_k $.
    4. Training uses Cross-Entropy Loss applied to the predicted distribution and the true labels.
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