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AugeLab Studio Manual
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Metrics Accuracy

Metrics Accuracy Node Documentation

The Metrics Accuracy node in AugeLab Studio represents the accuracy metric for evaluating a model's performance in a deep learning task.

Node Overview

The Metrics Accuracy node calculates the accuracy of a model's predictions by comparing them to the true labels. It has the following properties:
  • Node Title: Metrics Accuracy
  • Node ID: OP_NODE_AI_METRICS_ACCURACY

Inputs

The Metrics Accuracy node does not have any input sockets. It operates on the output of the model to evaluate its accuracy.

Outputs

The Metrics Accuracy node outputs the accuracy metric, which can be used for evaluating the performance of the model.

Node Interaction

  1. 1.
    Drag and drop the Metrics Accuracy node from the node library onto the canvas in AugeLab Studio.
  2. 2.
    Connect the output of the model to the Metrics Accuracy node.
  3. 3.
    The accuracy metric will be calculated and outputted by the node.

Implementation Details

The Metrics Accuracy node is implemented as a subclass of the NodeCNN base class. It overrides the evalAi method to return the accuracy metric.
  • The Metrics Accuracy node does not have any input sockets.
  • The node calculates the accuracy metric using the CategoricalAccuracy metric from the Keras library.
  • The accuracy metric is a standard metric for classification tasks.
  • The node returns the accuracy metric as the output.

Usage

  1. 1.
    Drag and drop the Metrics Accuracy node from the node library onto the canvas in AugeLab Studio.
  2. 2.
    Connect the output of the model to the Metrics Accuracy node.
  3. 3.
    Use the accuracy metric for evaluating the performance of the model.
  4. 4.
    Combine the accuracy metric with other evaluation metrics to gain further insights into the model's performance.

Notes

  • The Metrics Accuracy node calculates the accuracy of a model's predictions.
  • It expects the Keras library to be installed.
  • The node does not require any additional configuration.
  • The accuracy metric is a common metric for evaluating classification models.
  • It measures the percentage of correctly predicted labels.
  • The accuracy metric can be used in various deep learning tasks, such as image classification or text classification.
  • Connect the output of the model to the Metrics Accuracy node to evaluate the accuracy of the predictions.
  • The accuracy metric can be combined with other evaluation metrics, such as precision, recall, or F1 score, to get a comprehensive view of the model's performance.
  • The Metrics Accuracy node is particularly useful for monitoring the training progress and evaluating the final performance of the model.