Binary and multi-class classification code change

Question

I am using almost similar code that I found here...

https://towardsdatascience.com/classify-butterfly-images-with-deep-learning-in-keras-b3101fe0f98

The example is related to binary classification. The data that I am testing with is calling for multi-class classification. I guess I need to change activation and loss function. Can I use the same code found here if I have more than 2 types?

https://github.com/bertcarremans/Vlindervinder/blob/master/model/CNN.ipynb

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update: I have one more question. Is augmentation necessary if I have enough data?

Answer 1

Just change binary_crossentropy to categorical_crossentropy:

cnn.compile(loss='categorical_crossentropy',
            optimizer='rmsprop',
            metrics=['accuracy'])

If your labels are not one-hot encoded modify these lines:

train_generator = train_datagen.flow_from_directory(
    'data/train',
    target_size=(IMG_SIZE,IMG_SIZE),
    batch_size = BATCH_SIZE,
    class_mode='categorical')

validation_generator = validation_datagen.flow_from_directory(
    'data/validation',
    target_size=(IMG_SIZE,IMG_SIZE),
    batch_size = BATCH_SIZE,
    class_mode='categorical')