Tensorflow: Using Masking in Combination with Concatenate Layer

Use a Custom Layer to Propagate the Mask

Subclass Concatenate and override the compute_mask method:

from tensorflow.keras.layers import Concatenate

class MaskedConcatenate(Concatenate):

    def compute_mask(self, inputs, mask=None):

        if mask is None:

            return None

        # Assume masks are identical in time dimension

        return mask[0] if isinstance(mask, (list, tuple)) else mask

Use MaskedConcatenate() instead of the standard Concatenate.

Recompute the Mask After Concatenation

After concatenating, you can apply a Masking layer or tf.sequence_mask to recreate the mask if needed.

from tensorflow.keras.layers import Masking

# After concatenate

x = Concatenate()([x1, x2])

x = Masking(mask_value=0.0)(x)  # Reapply mask

Use Functional Alternatives

If you’re combining sequences, consider whether Add, Multiply, or attention layers (which support masks) are better suited than raw concatenation.