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Optimizer Nadam
The
Optimizer Nadam
node in AugeLab Studio represents the Nadam optimizer for deep learning tasks.The
Optimizer Nadam
node allows you to create a Nadam optimizer for training deep learning models. It has the following properties:- Node Title: Optimizer Nadam
- Node ID: OP_NODE_AI_OPT_NADAM
The
Optimizer Nadam
node does not require any inputs.The
Optimizer Nadam
node outputs the created Nadam optimizer.- 1.Drag and drop the
Optimizer Nadam
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 constant for numerical stability.
- 3.The Nadam optimizer will be created based on the specified configuration.
- 4.Use the output Nadam optimizer for training deep learning models.
The
Optimizer Nadam
node is implemented as a subclass of the NodeCNN
base class. It overrides the evalAi
method to create the Nadam optimizer.- The node validates the input values for the learning rate, beta 1, beta 2, and epsilon.
- The Nadam optimizer is created using the specified configuration.
- The created optimizer is returned as the output.
- 1.Drag and drop the
Optimizer Nadam
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 exponential decay of past gradients.
- Beta 2: Specify the exponential decay rate for the second moment estimates. It affects the exponential decay of past squared gradients.
- Epsilon: Specify a small constant for numerical stability. It prevents division by zero.
- 3.The Nadam optimizer will be created based on the specified configuration.
- 4.Use the output Nadam optimizer for training deep learning models.
- 5.Connect the output Nadam optimizer to the appropriate nodes for training, such as the
Model Training
node or theKeras Fit
node.
- The
Optimizer Nadam
node allows you to create a Nadam optimizer for training deep learning models. - It expects the Keras library to be installed.
- The Nadam optimizer is an extension of the Adam optimizer that incorporates Nesterov momentum.
- 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 affects the exponential decay of past gradients. Experiment with different values to achieve the desired decay behavior.
- The beta 2 parameter affects the exponential decay of past squared gradients. Experiment with different values to achieve the desired decay behavior.
- The epsilon parameter is a small constant for numerical stability. Experiment with different values to prevent division by zero.
- Connect the output Nadam optimizer to the appropriate nodes for training, such as the
Model Training
node or theKeras Fit
node. - The
Optimizer Nadam
node is particularly useful for training deep learning models with improved convergence and stability. - Experiment with different learning rates, beta 1 values, beta 2 values, and epsilon values to achieve optimal results for your training tasks.
- Combine the Nadam optimizer with other nodes and techniques to fine-tune your deep learning models and improve performance.