How to evaluate and test models in Keras?

9 months ago

Liam

2 minutes

In Keras, you can use the evaluate() method of a model to assess and test the model. This method takes input data and labels as parameters, and returns the performance metrics of the model on the test data. For example:

loss, accuracy = model.evaluate(test_data, test_labels)

In this case, test_data refers to the testing data and test_labels are the corresponding labels. This method returns the model’s loss value and accuracy on the testing data. Different evaluation metrics such as accuracy, precision, recall, etc. can be chosen as needed.

Additionally, you can use the predict() method to make predictions with the model, and then calculate various evaluation metrics based on the predicted results and actual labels. For example:

predictions = model.predict(test_data)

You can then calculate metrics such as accuracy, precision, and recall based on predictions and test_labels. You can use relevant functions from the scikit-learn library for this calculation.

In Keras, you can evaluate and test a model using the evaluate() and predict() methods, selecting the appropriate method and evaluation metrics as needed.