Training Parameters

This function block allows users to specify parameters for training an artificial intelligence (AI) model, particularly in the context of a convolutional neural network (CNN). Users can define the batch size and the number of epochs for the training process.

📥 Inputs

This block does not require any inputs.

📤 Outputs

Training Parameters Returns a dictionary containing the specified batch size and epochs for training the AI model.

🕹️ Controls

Batch Size A text field where users can specify the number of training samples to be processed before the model's internal parameters are updated. The default value is set to 32.

Epochs A text field where users can specify the number of complete passes through the training dataset. The default value is set to 150.

🎨 Features

Dynamic Parameter Setting Users can adjust the batch size and epochs directly from the interface, allowing for flexible model training configurations.

Validation Check Each input field validates its number type, ensuring correct data types are provided for training parameters.

📝 Usage Instructions

1. Open the Block: Drag and drop the block into your flow.

2. Set Parameters: Enter the desired values for Batch Size and Epochs. Adjust these values to fit your training needs.

3. Run the Training: Use the output from this block in conjunction with your AI training setup.

📊 Evaluation

When executed, this function block will output the selected training parameters in a structured format ready to be utilized for AI model training.

💡 Tips and Tricks

Choosing Batch Size

• A larger batch size may lead to faster training during each epoch, but can result in less generalization. If you have limited memory, you may want to use a smaller batch size.

Choosing Epochs

• Start training with a baseline of 10-50 epochs and adjust based on training results. Monitoring for overfitting is essential; stop training if the validation accuracy no longer improves.

Using with Other Blocks

• Combine this block with Node AI Training which will utilize the specified parameters to effectively initiate the training process of your AI model.

🛠️ Troubleshooting

Invalid Input

If you receive an error stating that the input is invalid, ensure that the values entered in both Batch Size and Epochs are numeric and within a reasonable range for your model.