98c6d3bc803e5d04be425e6b2eabca3ea707ed0e,finetune/nn/target_blocks.py,,_apply_class_weight,#,134

Before Change

        weights = (
            tf.reduce_sum(input_tensor=class_weights * tf.cast(targets, dtype=tf.float32), axis=1)
        )
        weights *= tf.cast(tf.reduce_prod(input_tensor=tf.shape(input=weights)), dtype=tf.float32) / tf.reduce_sum(
            input_tensor=weights
        )
        losses *= tf.expand_dims(weights, 1)
    return losses

After Change

        weights = tf.reduce_sum(
            input_tensor=class_weights * tf.cast(targets, dtype=tf.float32), axis=1
        )
        weights *= tf.math.divide_no_nan(
            tf.cast(
                tf.reduce_prod(input_tensor=tf.shape(input=weights)), dtype=tf.float32
            ),
            tf.reduce_sum(input_tensor=weights),
        )
        losses *= tf.expand_dims(weights, 1)
    return losses

Instances