# Conv. Sep. Layer 2D

This function block utilizes a separable convolution layer, which is a vital component in neural networks for processing image data. It focuses on optimizing the convolution operation by separating it into depthwise and pointwise convolutions, improving efficiency without sacrificing performance.

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

The specific inputs will depend on the configurations set within the parent `NodeCNN`. Generally, this block receives features from the previous block, which might include image data or feature maps.

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

The block outputs processed feature maps after applying the separable convolution, which can be utilized in subsequent layers for further processing or classification.

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

`Kernel Size` This control allows you to set the size of the convolution kernel, which determines the size of the region that will be focused on during the convolution operation.

`Activation Function` Choose the activation function to apply after the convolution operation, such as ReLU, Sigmoid, etc.

`Filters` Specify the number of filters to be used in the layer, impacting how many feature maps will be generated.

`Padding` Decide whether to use padding, which can affect the dimensionality of the output.

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

`Layer Optimization` By implementing separable convolutions, this layer optimizes computational efficiency while maintaining the effectiveness of feature extraction.

`Dynamic Configurations` Users can adjust kernel sizes, activation functions, and filters to customize the convolution processing based on specific image data or use cases.

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

1. **Connect to Previous Layer**: Ensure the input is connected to a preceding layer that provides suitable feature data for convolution.
2. **Configure Layer Parameters**: Set the desired number of filters, kernel size, and activation functions as per your model requirements.
3. **Evaluate**: Run the block to process the input data through the separable convolution layer.

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

When executed, the function block applies the separable convolution process on the input data, creating refined feature maps that can be utilized in downstream layers of your neural network.

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

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<summary>Kernel Size Impacts</summary>

Experiment with different kernel sizes. Smaller kernels can effectively extract more features from the fine details of images, while larger kernels can capture more context.

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<summary>Adjusting Filters</summary>

Increasing the number of filters allows the model to capture more complex features. Start with a small number and increase as needed, based on validation performance.

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<summary>Monitor Performance</summary>

Use `Debug Input` or `Image Logger` to check the outputs at this layer, ensuring that the features are being extracted correctly and effectively.

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

<details>

<summary>No Output Produced</summary>

Ensure that there is a valid connection from the preceding layer and that the inputs are in the expected shape for processing.

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<summary>Invalid Layer Configuration</summary>

Double-check your configurations such as kernel size, filters, and activation functions to ensure they are set appropriately for your model structure.

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