How to find wrong prediction cases in test set (CNNs using Keras)

Editing as was not clear earlier

To identify the image files that are wrongly classified, you can use:

fnames = test_generator.filenames ## fnames is all the filenames/samples used in testing
errors = np.where(y_pred != test_generator.classes)[0] ## misclassifications done on the test data where y_pred is the predicted values
for i in errors:
    print(fnames[i])

Simply use model.predict_classes() and compare the output with true labes. i.e:

incorrects = np.nonzero(model.predict_class(X_test).reshape((-1,)) != y_test)

to get indices of incorrect predictions