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Batch Normalization

Batch Normalization Node Documentation
The Batch Normalization node in AugeLab Studio performs batch normalization on the input tensor. It normalizes the activations of the previous layer in a deep neural network, making the training process more stable and accelerating convergence.
Node Overview
The Batch Normalization node takes an input tensor and applies batch normalization to it. It provides the following outputs:
Output Tensor: The result of applying batch normalization to the input tensor.
The node does not have any adjustable parameters or properties.
Node Properties
Node Title: Batch Normalization
Node ID: OP_NODE_AI_BATCH_NORM
Inputs
The Batch Normalization node has one input socket:
1.Input Tensor: The input tensor to be subjected to batch normalization.
Outputs
The Batch Normalization node has one output socket:
1.Output Tensor: The result of applying batch normalization to the input tensor.
Node Interaction
1.Drag and drop the Batch Normalization node from the node library onto the canvas in AugeLab Studio.
2.Connect the input tensor to the Input Tensor input socket of the Batch Normalization node.
3.Run the pipeline.
4.The output socket of the Batch Normalization node will provide the output tensor after applying batch normalization.
Implementation Details
The Batch Normalization node uses the BatchNormalization layer from the Keras API to perform batch normalization. The node creates an instance of the BatchNormalization layer and applies it to the input tensor to produce the output tensor.
Batch normalization normalizes the activations of the previous layer by subtracting the mean and dividing by the standard deviation. It also introduces learnable parameters to scale and shift the normalized outputs. Batch normalization helps in improving the stability of training by reducing the internal covariate shift problem and allows for faster convergence.
Usage
1.Drag and drop the Batch Normalization node from the node library onto the canvas in AugeLab Studio.
2.Connect the input tensor to the Input Tensor input socket of the Batch Normalization node.
3.Run the pipeline.
4.The output socket of the Batch Normalization node will provide the output tensor after applying batch normalization.
Notes
The Batch Normalization node is commonly used in deep neural networks to normalize the activations and stabilize the training process.
Batch normalization helps in accelerating convergence, improving generalization, and reducing overfitting.
Ensure that the input tensor is correctly connected to the Input Tensor input socket of the Batch Normalization node.
The Batch Normalization node does not have any adjustable parameters or properties.
The output tensor can be further processed or used as input to other nodes in the pipeline.

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