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# Optimizer FTRL

The

`Optimizer FTRL`

node in AugeLab Studio represents the FTRL optimizer for deep learning tasks.The

`Optimizer FTRL`

node allows you to create an FTRL optimizer for training deep learning models. It has the following properties:- Node Title: Optimizer FTRL
- Node ID: OP_NODE_AI_OPT_FTRL

The

`Optimizer FTRL`

node does not require any inputs.The

`Optimizer FTRL`

node outputs the created FTRL optimizer.- 1.Drag and drop the
`Optimizer FTRL`

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.
- Learning Rate Power: Specify the power of the learning rate decay.
- Initial Accumulator Value: Specify the initial accumulator value.
- L1 Regularization: Specify the L1 regularization strength.
- L2 Regularization: Specify the L2 regularization strength.
- L2 Regularization Shrinkage: Specify the shrinkage strength for L2 regularization.

- 3.The FTRL optimizer will be created based on the specified configuration.
- 4.Use the output FTRL optimizer for training deep learning models.

The

`Optimizer FTRL`

node is implemented as a subclass of the `NodeCNN`

base class. It overrides the `evalAi`

method to create the FTRL optimizer.- The node validates the input values for the learning rate, learning rate power, initial accumulator value, L1 regularization, L2 regularization, and L2 regularization shrinkage.
- The FTRL optimizer is created using the specified configuration.
- The created optimizer is returned as the output.

- 1.Drag and drop the
`Optimizer FTRL`

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.
- Learning Rate Power: Specify the power of the learning rate decay. It affects the learning rate decay schedule.
- Initial Accumulator Value: Specify the initial accumulator value. It affects the weight update calculations.
- L1 Regularization: Specify the L1 regularization strength. It encourages sparse weights.
- L2 Regularization: Specify the L2 regularization strength. It prevents overfitting.
- L2 Regularization Shrinkage: Specify the shrinkage strength for L2 regularization. It affects the weight update calculations.

- 3.The FTRL optimizer will be created based on the specified configuration.
- 4.Use the output FTRL optimizer for training deep learning models.
- 5.Connect the output FTRL optimizer to the appropriate nodes for training, such as the
`Model Training`

node or the`Keras Fit`

node.

- The
`Optimizer FTRL`

node allows you to create an FTRL optimizer for training deep learning models. - It expects the Keras library to be installed.
- The FTRL optimizer is designed for sparse feature learning and is particularly useful for large-scale linear models.
- The learning rate controls the step size during training. Experiment with different learning rates to find the optimal value for your specific task.
- The learning rate power affects the learning rate decay schedule. Experiment with different values to achieve the desired decay behavior.
- The initial accumulator value affects the weight update calculations. Experiment with different values to find the optimal initialization.
- The L1 regularization strength encourages sparse weights. Experiment with different values to control the sparsity of the model.
- The L2 regularization strength prevents overfitting. Experiment with different values to control the amount of regularization applied.
- The L2 regularization shrinkage affects the weight update calculations. Experiment with different values to find the optimal shrinkage behavior.
- Connect the output FTRL optimizer to the appropriate nodes for training, such as the
`Model Training`

node or the`Keras Fit`

node. - The
`Optimizer FTRL`

node is particularly useful for training linear models and handling large-scale feature learning tasks. - Experiment with different learning rates, learning rate power values, initial accumulator values, L1 regularization strengths, L2 regularization strengths, and L2 regularization shrinkage values to achieve optimal results for your training tasks.
- Combine the FTRL optimizer with other nodes and techniques to fine-tune your deep learning models and improve performance.