Create a custom loss and accuracy functions for the seq2seq model

loss_acc_fn_seq2seq.py

def loss_func(targets, logits):
    crossentropy = tf.keras.losses.SparseCategoricalCrossentropy(
        from_logits=True)
    # Mask padding values, they do not have to compute for loss
    mask = tf.math.logical_not(tf.math.equal(targets, 0))
    mask = tf.cast(mask, dtype=tf.int64)
    # Calculate the loss value
    loss = crossentropy(targets, logits, sample_weight=mask)

    return loss

def accuracy_fn(y_true, y_pred):
    # y_pred shape is batch_size, seq length, vocab size
    # y_true shape is batch_size, seq length
    pred_values = K.cast(K.argmax(y_pred, axis=-1), dtype='int32')
    correct = K.cast(K.equal(y_true, pred_values), dtype='float32')

    # 0 is padding, don't include those
    mask = K.cast(K.greater(y_true, 0), dtype='float32')
    n_correct = K.sum(mask * correct)
    n_total = K.sum(mask)
  
    return n_correct / n_total