b5c8bdd81be77eac5434a476f62c5bdef56f1838,texar/modules/embedders/position_embedders.py,SinusoidsPositionEmbedder,__init__,#SinusoidsPositionEmbedder#Any#Any#,251

Before Change

        scaled_time = tf.expand_dims(positions, 1) \
            * tf.expand_dims(inv_timescales, 0)
        signal = tf.concat([tf.sin(scaled_time), tf.cos(scaled_time)], axis=1)
        signal = tf.pad(signal, [[0, 0], [0, tf.mod(dim, 2)]])
        self.signal = signal

    def default_hparams(self):
        Returns a dictionary of hyperparameters with default values

After Change

        )
        self.inv_timescales = inv_timescales

        if self._cache_embeddings:
            if position_size is None:
                raise ValueError(
                    ""position_size" must not be None when "
                    ""cache_embeddings" is set to True"
                )
            positions = tf.to_float(tf.range(position_size, dtype=tf.int32))
            signal = self._compute_embeddings(positions)
            self.signal = signal

    @staticmethod
    def default_hparams():
        Returns a dictionary of hyperparameters with default values
        We use a geometric sequence of timescales starting with

Instances