c8523b2dc735a1b82ca6170e6ca349defe9f77fc,txtgen/modules/decoders/transformer_decoders.py,TransformerDecoder,_build,#TransformerDecoder#Any#Any#,65

Before Change

            tgt_embedding = inputs
        num_units = tf.shape(tgt_embedding).as_list()[2]
        if self.scale:
            tgt_embedding = tgt_embedding * tf.sqrt(num_units)
        if self._hparams.sinusoid:
            position_dec_embeds = layers.sinusoid_positional_encoding(tgt_embedding,
                    scope = "dec_pe")
        dec_input = tf.layers.dropout(tgt_embedding + position_dec_embeds,

After Change

        self.logits = tf.layers.dense(attended_dec, self._vocab_size)
        self.preds = tf.to_int32(tf.arg_max(self.logits, dimension=-1))
        //[batch, seq_len]
        print("self.preds:{}".format(self.preds.shape))
        return self.logits, self.preds

    @property
    def output_size(self):

Instances