# Softmax Layer

This function block implements the Softmax activation function commonly used in machine learning, particularly in multi-class classification problems. It outputs probabilities for each class based on the input data.

## 📥 Inputs <a href="#inputs" id="inputs"></a>

This function block does not have any inputs as it functions as a layer in a neural network.

## 📤 Outputs <a href="#outputs" id="outputs"></a>

This function block does not produce any direct outputs but serves as a layer for downstream layers that will utilize its probabilities for further processing.

## 🕹️ Controls <a href="#controls" id="controls"></a>

`Axis` A dropdown selection to determine the axis along which the Softmax function will be computed.

* Select `1` for applying the Softmax across the rows.
* Select `-1` for applying the Softmax across the columns.

## 🎨 Features <a href="#features" id="features"></a>

`Multi-Class Capability` Allows the softmax activation to be computed across specified axes for multi-class predictions.

`Simple Configuration` The axis selection is straightforward, facilitating easy model configuration for users.

## 📝 Usage Instructions <a href="#usage" id="usage"></a>

1. **Select Axis**: Use the dropdown menu to choose the axis for the Softmax function (either `1` or `-1`).
2. **Integrate into Model**: Once configured, this block can be integrated into a neural network model where softmax probabilities are required.

## 📊 Evaluation <a href="#evaluation" id="evaluation"></a>

When utilized within a neural network model, this block will process inputs and provide softmax probabilities, thereby aiding in classification tasks.

## 💡 Tips and Tricks <a href="#tips-and-tricks" id="tips-and-tricks"></a>

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<summary>Multi-Class Output</summary>

The Softmax layer is particularly useful when your output dimension corresponds to multiple classes. Always ensure your preceding layer outputs the correct shape for the softmax function to perform effectively.

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<summary>Combining with Loss Functions</summary>

Consider combining this layer with a loss function like `Categorical Crossentropy` for multi-class classification to align your outputs with predictions effectively.

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<summary>Configuration of Axis</summary>

Pay special attention when selecting the axis. The choice could affect the model's ability to learn effectively, depending on your specific use case and how data is organized.

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## 🛠️ Troubleshooting <a href="#troubleshooting" id="troubleshooting"></a>

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<summary>Input Dimension Mismatch</summary>

Ensure that the dimensions of the input feeding into this layer match those expected by the softmax function for proper calculations. A mismatch may result in errors during model training.

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<summary>Softmax Probabilities Not Adding Up</summary>

If you notice that output probabilities do not sum to one, ensure that inputs provided to the softmax layer are logits. Logits are raw prediction scores from the preceding layer before applying softmax.

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