Test Accuracy Greater Than Train: What Does It Mean for Model Works Fine?

My test accuracy is 99% and train accuracy is 96% , does it mean model working excellently.

Model is DenseNet, images more than 1k per class (total 9 classes). Training set 11k images testing set 3k images.here is the confusion matrix

https://preview.redd.it/ta29d8cfjvxb1.png?width=820&format=png&auto=webp&s=b4dc84e8e63eafe288ab5099bb2ee8edf76427fb

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train_datagen = ImageDataGenerator(
rescale = 1./255,
rotation_range=30,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
vertical_flip=True)

test_datagen = ImageDataGenerator(rescale = 1./255)

train_ds = train_datagen.flow_from_directory(train_aug,
class_mode='categorical',
batch_size = 32,
target_size = (256, 256))

test_ds = test_datagen.flow_from_directory(test_dir,
class_mode='categorical',
batch_size = 32,
target_size …

accuracy deeplearning imagedatagenerator images matrix mean per rescale set test testing total train training