c8523b2dc735a1b82ca6170e6ca349defe9f77fc,examples/transformer.py,,,#,22

Before Change

    encoder_output = encoder(src_text["text_ids"][:, :-1],
            sequence_length=src_text["length"]-1)
    // Decode
    outputs, final_state, sequence_lengths = decoder(
        initial_state=connector(text_database._hparams.batch_size))

    // Build loss
    mle_loss = mle_losses.average_sequence_sparse_softmax_cross_entropy(
        labels=tgt_text["text_ids"][:, 1:],

After Change

            sequence_length=text_data_batch["source_length"])
    // Decode
    print("encoder_output:{}".format(encoder_output.shape))
    logits, preds = decoder(decoder_inputs, encoder_output)
    print("logits:{}".format(logits.shape))
    //istarget = tf.to_float(tf.not_equal(y, 0))
    // acc = tf.reduce_sum(tf.to_float(tf.equal(preds,

Instances