e9086f5d37f5f46b8327bf022c7b0c70d42460d1,magenta/models/shared/events_rnn_graph.py,,build_graph,#Any#Any#Any#,56

Before Change

        logits = tf.reshape(logits_flat, [hparams.batch_size, -1, num_classes])
        logits = logits[:, hparams.skip_first_n_losses:, :]
        logits_flat = tf.reshape(logits, [-1, num_classes])
        labels = labels[:, hparams.skip_first_n_losses:]

      labels_flat = tf.reshape(labels, [-1])
      mask_flat = tf.reshape(tf.sequence_mask(lengths, dtype=tf.float32), [-1])
      num_logits = tf.to_float(tf.reduce_sum(lengths))

After Change

      labels_flat = tf.reshape(labels, [-1])
      mask = tf.sequence_mask(lengths)
      if hparams.skip_first_n_losses:
        skip = tf.minimum(lengths, hparams.skip_first_n_losses)
        skip_mask = tf.sequence_mask(skip, maxlen=tf.reduce_max(lengths))
        mask = tf.logical_and(mask, tf.logical_not(skip_mask))
      mask = tf.cast(mask, tf.float32)
      mask_flat = tf.reshape(mask, [-1])

Instances