# Batch Normalization

This function block is used to apply batch normalization to neural network layers. Batch normalization improves the training speed and stability of deep networks by normalizing the output of a previous activation layer.

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

This function block does not have any direct inputs.

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

This function block does not produce direct outputs but facilitates improved performance for other connected layers in the model.

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

This function block does not have visible controls for user input; it operates automatically as part of the neural network architecture.

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

`Layer Integration` Integrates seamlessly within a neural network architecture, allowing for improved model performance.

`Training Stability` Helps maintain stable gradients during training, leading to faster convergence.

`Improved Performance` Ensures more efficient training and can lead to improved model accuracy.

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

1. **Integrate Into Model**: Connect this block into a neural network architecture where batch normalization is desired, typically after activation layers.
2. **Run Model Training**: Proceed with model training as this block will normalize the activations automatically during the training process.

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

When part of a training session, this function block will affect the performance of the network significantly by normalizing activations, thus potentially resulting in faster training and better convergence.

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

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<summary>Layer Ordering</summary>

When using batch normalization, place it immediately after the convolutional or dense layer and before the activation layer for optimal results.

</details>

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<summary>Tuning Hyperparameters</summary>

Experiment with different learning rates when using batch normalization, as normalization can reduce the sensitivity of the learning process.

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<summary>Combine with Dropout</summary>

Batch normalization can be effectively combined with dropout layers to help regularize the model further.

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

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<summary>Model Not Converging</summary>

If the model is not converging, ensure that batch normalization layers are placed correctly in the network and verify the learning rate settings.

</details>

<details>

<summary>Input Shape Issues</summary>

Ensure that the input shapes match as expected. Batch normalization requires the input tensor to be shaped correctly, typically resulting from preceding layers.

</details>
