labels = ["dog", "mammal", "cat", "fish", "rock"] #I have more
interesting_id = [0]*len(labels)
interesting_id[labels.index("rock")] = 1 #we only care about rock's accuracy
def single_class_accuracy(interesting_class_id):
    def single(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_preds with class_id_true for recall here
        accuracy_mask = K.cast(K.equal(class_id_preds, interesting_class_id), 'float32')
        class_acc_tensor = K.cast(K.equal(class_id_true, class_id_preds), 'float32') * accuracy_mask
        class_acc = K.sum(class_acc_tensor) / K.maximum(K.sum(accuracy_mask), 1)
        return class_acc
    return single

model.compile(optimizer=SGD(lr=0.0001, momentum=0.9),
              loss='binary_crossentropy', metrics=[metrics.binary_accuracy,
              single_class_accuracy(interesting_id)])