4d49910b3f0696102f813fb5ba451b934a4a579c,opennmt/utils/losses.py,,cross_entropy_sequence_loss,#Any#Any#Any#Any#Any#Any#Any#,28

Before Change

    dtype = cross_entropy.dtype

    if sequence_weight is not None:
        cross_entropy *= tf.expand_dims(tf.cast(sequence_weight, dtype), 1)

    if sequence_length is not None:
        max_time = tf.shape(logits)[1]
        mask = tf.sequence_mask(sequence_length, maxlen=max_time, dtype=dtype)

After Change

    if average_in_time or not training:
        loss_normalizer = loss_token_normalizer
    elif sequence_weight is not None:
        loss_normalizer = tf.reduce_sum(sequence_weight)
    else:
        loss_normalizer = tf.cast(batch_size, dtype)

    return loss, loss_normalizer, loss_token_normalizer

Instances