I am building a custom metric to measure the accuracy of one class in my multi-class dataset during training. I am having trouble selecting the class.
The targets are one hot (e.g: the class 0 label is [1 0 0 0 0] :
from keras import backend as K def single_class_accuracy(y_true, y_pred): idx = bool(y_true[:, 0]) # boolean mask for class 0 class_preds = y_pred[idx] class_true = y_true[idx] class_acc = K.mean(K.equal(K.argmax(class_true, axis=-1), K.argmax(class_preds, axis=-1))) # multi-class accuracy return class_acc The trouble is, we have to use Keras functions to index tensors. How do you create a boolean mask for a tensor? Thank you.
1 Answers
Answers 1
Note that when talking about the accuracy of one class one may refer to either of the following (not equivalent) two amounts:
- The precision, which, for class C, is the ratio of examples labelled with class C that are predicted to have class C.
- The recall, which, for class C, is the ratio of examples predicted to be of class C that are in fact labelled with class C.
Instead of doing complex indexing, you can just rely on masking for you computation. Assuming we are talking about precision here (changing to recall would be trivial).
from keras import backend as K INTERESTING_CLASS_ID = 0 # Choose the class of interest def single_class_accuracy(y_true, y_pred): class_id_true = K.argmax(y_true, axis=-1) class_id_preds = K.argmax(y_pred, axis=-1) # Replace class_id_true with class_id_preds for recall here accuracy_mask = K.cast(K.equal(class_id_true, INTERESTING_CLASS_ID), 'int32') class_acc_tensor = K.cast(K.equal(class_num_true, class_id_preds), 'int32') * accuracy_mask class_acc = K.sum(class_acc_tensor) / K.maximum(K.sum(accuracy_mask), 1) return class_acc If you want to be more flexible, you can also have the class of interest parametrised:
from keras import backend as K def single_class_accuracy(interesting_class_id): def fn(y_true, y_pred): class_id_true = K.argmax(y_true, axis=-1) class_id_preds = K.argmax(y_pred, axis=-1) # Replace class_id_true with class_id_preds for recall here accuracy_mask = K.cast(K.equal(class_id_true, interesting_class_id), 'int32') class_acc_tensor = K.cast(K.equal(class_num_true, class_id_preds), 'int32') * accuracy_mask class_acc = K.sum(class_acc_tensor) / K.maximum(K.sum(accuracy_mask), 1) return class_acc return fn And the use it as:
model.compile(..., metrics=[single_class_accuracy(INTERESTING_CLASS_ID)]) 
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