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Optimizer Adamax

Optimizer Adamax Node Documentation
The Optimizer Adamax node in AugeLab Studio represents the Adamax optimizer for deep learning tasks.
Node Overview
The Optimizer Adamax node allows you to create an Adamax optimizer for training deep learning models. It has the following properties:
Node Title: Optimizer Adamax
Node ID: OP_NODE_AI_OPT_ADAMAX
Inputs
The Optimizer Adamax node does not require any inputs.
Outputs
The Optimizer Adamax node outputs the created Adamax optimizer.
Node Interaction
1.Drag and drop the Optimizer Adamax 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.
Beta 1: Specify the exponential decay rate for the first moment estimates.
Beta 2: Specify the exponential decay rate for the second moment estimates.
Epsilon: Specify a small value for numerical stability.
3.The Adamax optimizer will be created based on the specified configuration.
4.Use the output Adamax optimizer for training deep learning models.
Implementation Details
The Optimizer Adamax node is implemented as a subclass of the NodeCNN base class. It overrides the evalAi method to create the Adamax optimizer.
The node validates the input values for the learning rate, beta 1, beta 2, and epsilon.
The Adamax optimizer is created using the specified learning rate, beta 1, beta 2, and epsilon.
The created optimizer is returned as the output.
Usage
1.Drag and drop the Optimizer Adamax 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.
Beta 1: Specify the exponential decay rate for the first moment estimates. It affects the weight update calculations.
Beta 2: Specify the exponential decay rate for the second moment estimates. It affects the weight update calculations.
Epsilon: Specify a small value for numerical stability. It prevents division by zero.
3.The Adamax optimizer will be created based on the specified configuration.
4.Use the output Adamax optimizer for training deep learning models.
5.Connect the output Adamax optimizer to the appropriate nodes for training, such as the Model Training node or the Keras Fit node.
Notes
The Optimizer Adamax node allows you to create an Adamax optimizer for training deep learning models.
It expects the Keras library to be installed.
The Adamax optimizer is a variant of the Adam optimizer.
The learning rate controls the step size during training. Experiment with different learning rates to find the optimal value for your specific task.
The beta 1 parameter controls the exponential decay rate for the first moment estimates (the moving average of the gradient). It affects the weight update calculations.
The beta 2 parameter controls the exponential decay rate for the second moment estimates (the moving average of the absolute value of the gradient). It affects the weight update calculations.
The epsilon parameter is a small value used for numerical stability. It prevents division by zero.
Connect the output Adamax optimizer to the appropriate nodes for training, such as the Model Training node or the Keras Fit node.
The Optimizer Adamax node is particularly useful for training deep learning models and adjusting the optimization process.
Experiment with different learning rates, beta 1 values, beta 2 values, and epsilon values to achieve optimal results for your training tasks.
Combine the Adamax optimizer with other nodes and techniques to fine-tune your deep learning models and improve performance.

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