Optimizer SGD

This function block provides an implementation of the Stochastic Gradient Descent (SGD) optimization algorithm, widely used in training machine learning models.

📥 Inputs

This function block does not require any inputs.

📤 Outputs

The output of this block is the configured optimizer that can be used for model training.

🕹️ Controls

Learning rate A text input that specifies the learning rate for the optimizer. This is a crucial hyperparameter that controls how much to change the model in response to the estimated error each time the model weights are updated.

Momentum A text input that specifies the momentum value. This is a technique that helps accelerate SGD in the relevant direction and dampens oscillations.

Centered A dropdown menu allowing you to choose whether to activate the centered version of the Nesterov Accelerated Gradient. This option can give improved convergence properties for certain problems.

🎨 Features

Customizable Hyperparameters Users can customize the learning rate, momentum, and whether to use Nesterov momentum.

Integration with Keras This block leverages Keras for optimization, enabling easy integration with Keras-based deep learning models.

📝 Usage Instructions

1. Set Learning Rate: Input the desired learning rate in the Learning rate field. A common starting point is 0.001.

2. Set Momentum: Input the momentum value in the Momentum field. If not needed, leave it as 0.

3. Toggle Nesterov: Select whether you want to use the centered Nesterov momentum from the Centered dropdown.

4. Evaluate: Execute the block to configure the SGD optimizer based on the provided parameters.

📊 Evaluation

When evaluated, this function block produces a configured SGD optimizer that can be utilized in the training phase of a machine learning model.

💡 Tips and Tricks

Choosing a Learning Rate

If unsure about the learning rate, start with 0.001, then adjust based on the performance of the model during training.

Using Momentum

Using a momentum value of around 0.9 is common practice, as it can help smooth out the updates and improve convergence speed.

Experiment with Nesterov

If you notice oscillations during training, try enabling Nesterov momentum. This may enhance the convergence speed and stability of gradient descent.

🛠️ Troubleshooting

Error with Learning Rate

Make sure that the learning rate input value is a valid float. If you encounter any error messages, check the format of your input.

Optimizer Configuration Issues

Double-check to ensure all fields are filled properly before evaluating the block. Unconfigured parameters may lead to errors during the optimization process.