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Output Layer of Softmax Regression
The output layer of softmax regression generates a probability distribution , where each entry represents the predicted probability that the input belongs to a particular class. These probabilities are calculated by applying the softmax activation function to the raw output scores . To ensure the outputs represent valid probabilities, the operation exponentiates each score and normalizes it by the sum of all exponentiated scores:
This transforms the unconstrained linear model outputs into a valid probability distribution.
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Ch.1 Pre-training - Foundations of Large Language Models
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What is Softmax Regression and How is it Related to Logistic Regression?
The Softmax Function, Simplified
Output Layer of Softmax Regression
Implementation of Softmax Regression Using Numpy
Implementation of Softmax Regression Using Tensorflow
Cross-Entropy Loss for Softmax Regression
Vectorized Minibatch Softmax Regression
Softmax Function Definition
A vector of raw, unnormalized scores
[1000, 1002, 999]is passed as input to a computational function that converts these scores into a probability distribution. A common technique to prevent numerical errors is to first subtract the maximum value of the vector from every element before applying the main transformation (exponentiation). Why is this subtraction step crucial for handling large input values?Calculating Output Probabilities from Model Scores
A model outputs the following raw, unnormalized scores for three classes:
[2.0, 1.0, 0.1]. If a constant value of 5.0 is added to each of these scores, resulting in a new score vector of[7.0, 6.0, 5.1], how will the resulting probability distribution calculated by the function that converts these scores to probabilities change?Order Preservation of the Softmax Function
Energy-Based View of Softmax
Output Layer of Softmax Regression
Partition Function in Softmax
Vectorized Minibatch Softmax Regression
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Sample Calculation of Softmax Output Layer
Example of a SoftMax activation transformation
Maximum Probability Decision Rule
Consider a 4-class classification problem where the final layer of a model produces the following pre-activation scores for a single input:
[1.0, 2.0, 1.5, 5.0]. The model then uses an activation function that exponentiates each score and normalizes the results to produce a probability distribution. Without performing the full calculation, which of the following statements best describes the resulting probability distribution?Calculating an Output Probability
Classifier Output Analysis
Computational Cost of Fully Connected Layers