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## Softmax Activation Function Implementation (easy)

#### Example

## Understanding the Softmax Activation Function

The softmax function is a generalization of the sigmoid function and is used in the output layer of a neural network model that handles multi-class classification tasks.
### Mathematical Definition

The softmax function is mathematically represented as:
\[
\text{softmax}(z_i) = \frac{e^{z_i}}{\sum_{j} e^{z_j}}
\]
### Characteristics

Write a Python function that computes the softmax activation for a given list of scores. The function should return the softmax values as a list, each rounded to four decimal places.

Example: input: scores = [1, 2, 3] output: [0.0900, 0.2447, 0.6652] reasoning: The softmax function converts a list of values into a probability distribution. The probabilities are proportional to the exponential of each element divided by the sum of the exponentials of all elements in the list.

**Output Range:**Each output value is between 0 and 1, and the sum of all outputs is 1.**Purpose:**It transforms scores into probabilities, which are easier to interpret and are useful for classification.

import math def softmax(scores: list[float]) -> list[float]: exp_scores = [math.exp(score) for score in scores] sum_exp_scores = sum(exp_scores) probabilities = [round(score / sum_exp_scores, 4) for score in exp_scores] return probabilities

Output will be shown here.

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