# Compile Model

This function block is used to compile a neural network model for 2D data processing in machine learning tasks. It integrates various model parameters, including layers, optimizers, loss functions, and metrics.

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

`Layer` This input connects to the desired neural network layer to be compiled into the model.

`Optimizer` This input specifies the optimization algorithm to be used in training the model.

`Loss Function` This input connects to the chosen loss function that will guide the optimization process.

`Metrics` This input connects to the performance metrics used to evaluate the model during training.

`Training Params` This input contains parameters related to the training process, such as paths and configurations.

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

This function block does not provide any outputs.

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

`Compile Model` This button initiates the compilation of the neural network model based on the provided inputs.

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

`Session Management` Clears any existing TensorFlow sessions before compiling the model to ensure a clean state.

`Error Handling` Provides detailed error messages and logging to guide users when inputs are missing or invalid.

`Training Window Launching` Successfully compile the model and launch a training window for iterative training processes.

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

1. **Connect Inputs**: Link appropriate layers to the `Layer` input, optimizers to the `Optimizer` input, loss functions to the `Loss Function` input, and performance metrics to the `Metrics` input.
2. **Configure Training Parameters**: Ensure that valid training parameters are connected to the `Training Params` input.
3. **Compile the Model**: Click the `Compile Model` button to initiate the model compilation process.
4. **Training**: On successful compilation, a training window will appear for further steps in the training process.

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

Upon successful execution, this function block compiles the model according to the specified parameters and prepares it for training.

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

<details>

<summary>Ensure Valid Inputs</summary>

Always check that all required inputs are connected before attempting to compile the model. Missing inputs can lead to compilation errors.

</details>

<details>

<summary>Use Correct Paths</summary>

Make sure that the training path specified in the training parameters exists and is accessible. Incorrect paths will result in validation errors.

</details>

<details>

<summary>More Info on Parameters</summary>

If you're unsure about the parameters required by the model or their structure, consult the model documentation or use visual aids like flowcharts to outline required connections.

</details>

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

<details>

<summary>Compilation Errors</summary>

If an error occurs during compilation, review the provided inputs for correctness and ensure all components are properly configured.

</details>

<details>

<summary>Model Not Training</summary>

If the model compiles successfully but does not train, verify your training parameters—especially those related to paths, classes, and optimizers.

</details>
