Metrics Accuracy

This function block calculates the accuracy of predictions made by a machine learning model, specifically designed for multi-class classification problems using categorical metrics.

📥 Inputs

This function block does not accept any inputs.

📤 Outputs

Categorical Accuracy This output provides the accuracy metric calculated based on model predictions compared to true labels.

🕹️ Controls

This block does not contain adjustable controls.

🎨 Features

Categorical Accuracy Calculation Automatically computes the accuracy of model predictions against the actual classifications.

Integration with Keras This block is designed to seamlessly integrate with Keras, making it easy to track model performance metrics during training or evaluation.

📝 Usage Instructions

1. Link the Block: Ensure this block is connected to a model output that already provides predictions.

2. Run the Evaluation: Execute the block, and it will compute the accuracy metric of the model predictions.

3. Retrieve Results: Access the output to evaluate how well the model is performing in classifying the input data.

📊 Evaluation

Upon execution, this function block will provide the accuracy of the model predictions, allowing users to gauge the model's effectiveness.

🛠️ Troubleshooting

Accuracy Not Changing

If the accuracy remains unchanged, make sure you're feeding in the correct predictions and true labels for evaluation. Checking the model training process may also help identify the cause.

No Outputs Generated

If you notice that there are no outputs, ensure that the model connected is properly configured and performing predictions before this block is evaluated.