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Optimizer SGD

Optimizer SGD Node Documentation
The Optimizer SGD node in AugeLab Studio represents the SGD (Stochastic Gradient Descent) optimizer for deep learning tasks.
Node Overview
The Optimizer SGD node allows you to create an SGD optimizer for training deep learning models. It has the following properties:
Node Title: Optimizer SGD
Node ID: OP_NODE_AI_OPT_SGD
Inputs
The Optimizer SGD node does not require any inputs.
Outputs
The Optimizer SGD node outputs the created SGD optimizer.
Node Interaction
1.Drag and drop the Optimizer SGD node from the node library onto the canvas in AugeLab Studio.
2.Configure the node properties:
Learning Rate: Specify the learning rate for the optimizer.
Momentum: Specify the momentum term.
Nesterov: Specify whether to use Nesterov momentum.
3.The SGD optimizer will be created based on the specified configuration.
4.Use the output SGD optimizer for training deep learning models.
Implementation Details
The Optimizer SGD node is implemented as a subclass of the NodeCNN base class. It overrides the evalAi method to create the SGD optimizer.
The node validates the input values for the learning rate, momentum, and Nesterov.
The SGD optimizer is created using the specified configuration.
The created optimizer is returned as the output.
Usage
1.Drag and drop the Optimizer SGD node from the node library onto the canvas in AugeLab Studio.
2.Configure the node properties:
Learning Rate: Specify the learning rate for the optimizer. This controls the step size during training.
Momentum: Specify the momentum term. It affects the contribution of the previous update to the current update.
Nesterov: Specify whether to use Nesterov momentum. Nesterov momentum enhances the SGD optimization algorithm by considering the future gradient update when computing the current update.
3.The SGD optimizer will be created based on the specified configuration.
4.Use the output SGD optimizer for training deep learning models.
5.Connect the output SGD optimizer to the appropriate nodes for training, such as the Model Training node or the Keras Fit node.
Notes
The Optimizer SGD node allows you to create an SGD optimizer for training deep learning models.
It expects the Keras library to be installed.
The SGD optimizer is a classic optimization algorithm widely used in deep learning.
The learning rate controls the step size during training. Experiment with different learning rates to find the optimal value for your specific task.
The momentum parameter affects the contribution of the previous update to the current update. Experiment with different values to achieve the desired momentum behavior.
The Nesterov parameter determines whether to use Nesterov momentum. Nesterov momentum enhances the SGD optimization algorithm by considering the future gradient update when computing the current update. Experiment with both Nesterov and non-Nesterov versions to evaluate their impact on training performance.
Connect the output SGD optimizer to the appropriate nodes for training, such as the Model Training node or the Keras Fit node.
The Optimizer SGD node is suitable for various deep learning tasks, especially when dealing with large datasets.
Experiment with different learning rates, momentum values, and Nesterov settings to achieve optimal results for your training tasks.
Combine the SGD optimizer with other nodes and techniques to fine-tune your deep learning models and improve performance.

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