fd5c54dfd76d06569d615634526eb631aab1d05f,open_seq2seq/optimizers/optimizers.py,,optimize_loss,#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,89

Before Change

                         tf.cast(tf.less(larc_grad_update, 1.0), tf.int32))
          larc_grad_update = tf.minimum(larc_grad_update, 1.0)
        larc_grad_update = tf.saturate_cast(larc_grad_update, var_dtype)
        summary.scalar("larc_grad_update/{}".format(v.name), larc_grad_update)
        gradients[idx] = (larc_grad_update * g, v)

    // Create gradient updates.
    grad_updates = opt.apply_gradients(

After Change

        gradients[idx] = (larc_grad_update * g, v)

        // adding additional summary
        if "larc_summaries" in summaries:
          summary.scalar("larc_grad_update/{}".format(v.name), larc_grad_update)
          summary.scalar("larc_grad_norm/{}".format(v.name),
                         tf.cast(g_norm, var_dtype) * larc_grad_update)

    // Create gradient updates.
    grad_updates = opt.apply_gradients(
        gradients,
        global_step=global_step if increment_global_step else None,
        name="train")

Instances