Keras - Precision and Recall is greater than 1 (Multi classification)

Question

I am working on a multi classification problem using CNN's in keras. My precision and recall score is always over 1 which does not make any sense at all. Attached below is my code, what am I doing wrong?

def recall(y_true, y_pred):
     true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
     possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
     recall = true_positives / (possible_positives + K.epsilon())
     return recall

def precision(y_true, y_pred):
    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
    precision = true_positives / (predicted_positives + K.epsilon())
    return precision

model.compile(loss='categorical_crossentropy', optimizer='Adam', metrics=['accuracy',recall,precision])

Answer 1

I was able to figure this out. The above code works perfectly once you one-hot encode all the categorical labels. Also, make sure you do NOT have sparse_categorical_crossentropy as your loss function, and instead just use categorical_crossentropy.

If you wish to convert your categorical values to one-hot encoded values in Keras, you can just use this code:

from keras.utils import to_categorical
y_train = to_categorical(y_train)

The reason you have to do the above is noted in Keras documentation:

"when using the categorical_crossentropy loss, your targets should be in categorical format (e.g. if you have 10 classes, the target for each sample should be a 10-dimensional vector that is all-zeros except for a 1 at the index corresponding to the class of the sample). In order to convert integer targets into categorical targets, you can use the Keras utility to_categorical"