Optimizer Nadam

This function block implements the Nadam optimizer, a popular optimization algorithm that combines the advantages of Adam and Nesterov accelerated gradients. It allows users to set various parameters associated with the optimizer.

📥 Inputs

This function block does not require any inputs.

📤 Outputs

The output of this block is the Nadam optimizer instance, which can be used in training neural networks.

🕹️ Controls

Learning Rate The rate at which the optimizer updates the model parameters. A typical default value is 0.001.

Beta 1 This parameter controls the exponential decay rate for the first moment estimates. The standard value is typically 0.9.

Beta 2 This parameter controls the exponential decay rate for the second-moment estimates. A common value is 0.999.

Epsilon A small constant added to improve numerical stability, usually set to 1e-07.

🎨 Features

Parameter Configuration Allows users to customize key parameters of the Nadam optimizer to suit their specific needs.

Real-time Updates Changes to the parameters can be made in real-time, allowing for immediate feedback in the optimization process.

📝 Usage Instructions

1. Set Parameters: Fill in the desired values for Learning Rate, Beta 1, Beta 2, and Epsilon using the provided input fields.

2. Evaluate: Run the block to create an instance of the Nadam optimizer based on the specified parameters.

📊 Evaluation

Upon evaluation, this block outputs the configured Nadam optimizer, which can be used in training a neural network.

💡 Tips and Tricks

Choosing Learning Rate

A good learning rate is crucial. If you encounter slow convergence, consider gradually increasing the learning rate. If convergence is oscillating, try lowering it.

Adjusting Beta Values

Experimenting with the beta_1 and beta_2 values can significantly impact optimizer performance. Typical values of 0.9 for beta_1 and 0.999 for beta_2 are recommended as starting points.

Using Epsilon

The default epsilon value of 1e-07 is often sufficient; however, adjusting it slightly can help in preventing division by zero errors in some edge cases.

🛠️ Troubleshooting

Parameter Value Errors

Ensure that the parameters for Learning Rate, Beta 1, Beta 2, and Epsilon are within reasonable ranges (e.g., Learning Rate should typically be greater than 0 and less than 1).

If you experience errors, verify the data type of each parameter.