python-kerasHow do I evaluate a model in Python using Keras?
Evaluating a model in Python using Keras is relatively straightforward. The following example code block demonstrates how to evaluate a model using the model.evaluate()
function:
# evaluate the model
scores = model.evaluate(X_test, y_test)
print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))
This code will output the accuracy score of the model on the test set:
acc: 98.00%
The code consists of the following parts:
-
model.evaluate()
: This is a Keras function that evaluates the model on a given dataset. It takes two arguments: the test data (X_test
) and the test labels (y_test
). -
model.metrics_names[1]
: This is a list of the metrics that the model is evaluated on, with the first element being the name of the metric (in this case,acc
for accuracy). -
scores[1]
: This is the score of the model on the given metric (in this case, accuracy). -
print()
: This prints the metric name and score to the console.
More information on evaluating models in Keras can be found in the Keras Documentation.
More of Python Keras
- How do I use Python Keras to zip a file?
- How do I use Python's tf.keras.utils.get_file to retrieve a file?
- How do I use zero padding in Python Keras?
- How can I use Python and Keras together?
- How can I use Python Keras online?
- How do I use the to_categorical function in Python Keras?
- How can I use Python Keras to develop a reinforcement learning model?
- How to load a model in Python Keras?
- How can I use XGBoost, Python and Keras together to build a machine learning model?
- What is Python Keras and how is it used?
See more codes...