You could use the [scikit-learn classification report][1]. To convert your labels into a numerical or binary format take a look at the [scikit-learn label encoder][2]. y_pred = model.predict(x_test, batch_size=64, verbose=1) y_pred_bool = np.argmax(y_pred, axis=1) print(classification_report(y_test, y_pred_bool)) which gives you (output copied from the scikit-learn example): precision recall f1-score support class 0 0.50 1.00 0.67 1 class 1 0.00 0.00 0.00 1 class 2 1.00 0.67 0.80 3 [1]: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html [2]: https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html