confusionreport.py

confusionreport.py

from sklearn.metrics import classification_report, confusion_matrix

TEST_SIZE=200
target_names = ['J', 'L']
Y_pred = model_convenet_ad_hoc.predict_generator(test_generator, TEST_SIZE)
Y_pred = Y_pred.flatten()
y_pred_class = np.where(Y_pred > 0.5, 1, 0)

print('Classification Report')
print(classification_report(test_generator.classes, y_pred_class, target_names=target_names))

print('Confusion Matrix')
plot_confusion_matrix(confusion_matrix(test_generator.classes, y_pred_class),target_names)