Tensorflow 2.0 Custom loss function with multiple inputs

Question

I am trying to optimize a model with the following two loss functions

def loss_1(pred, weights, logits):
    weighted_sparse_ce = kls.SparseCategoricalCrossentro         


        

Answer 1

In tf 1.x we have tf.nn.weighted_cross_entropy_with_logits function which allows us trade off recall and precision by adding extra positive weights for each class. In multi-label classification, it should be a (N,) tensor or numpy array. However, in tf 2.0, I haven't found similar loss functions yet, so I wrote my own loss function with extra arguments pos_w_arr.

from tensorflow.keras.backend import epsilon

def pos_w_loss(pos_w_arr):
    """
    Define positive weighted loss function
    """
    def fn(y_true, y_pred):
        _epsilon = tf.convert_to_tensor(epsilon(), dtype=y_pred.dtype.base_dtype)
        _y_pred = tf.clip_by_value(y_pred, _epsilon, 1. - _epsilon)
        cost = tf.multiply(tf.multiply(y_true, tf.math.log(
            _y_pred)), pos_w_arr)+tf.multiply((1-y_true), tf.math.log(1-_y_pred))
        return -tf.reduce_mean(cost)
    return fn

Not sure what do you mean it wouldn't work when using eager tensors or numpy array as inputs though. Please correct me if I'm wrong.