# tf.keras.layers.Masking

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|---|
| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/keras-team/keras/tree/v2.7.0/keras/layers/core/masking.py#L23-L87) |

Masks a sequence by using a mask value to skip timesteps.

Inherits From: [`Layer`](https://www.tensorflow.org/versions/r2.7/api_docs/python/tf/keras/layers/Layer), [`Module`](https://www.tensorflow.org/versions/r2.7/api_docs/python/tf/Module)

#### View aliases


**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

[`tf.compat.v1.keras.layers.Masking`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Masking)

<br />

    tf.keras.layers.Masking(
        mask_value=0.0, **kwargs
    )

For each timestep in the input tensor (dimension #1 in the tensor),
if all values in the input tensor at that timestep
are equal to `mask_value`, then the timestep will be masked (skipped)
in all downstream layers (as long as they support masking).

If any downstream layer does not support masking yet receives such
an input mask, an exception will be raised.

#### Example:

Consider a Numpy data array `x` of shape `(samples, timesteps, features)`,
to be fed to an LSTM layer. You want to mask timestep #3 and #5 because you
lack data for these timesteps. You can:

- Set `x[:, 3, :] = 0.` and `x[:, 5, :] = 0.`
- Insert a `Masking` layer with `mask_value=0.` before the LSTM layer:

    samples, timesteps, features = 32, 10, 8
    inputs = np.random.random([samples, timesteps, features]).astype(np.float32)
    inputs[:, 3, :] = 0.
    inputs[:, 5, :] = 0.

    model = tf.keras.models.Sequential()
    model.add(tf.keras.layers.Masking(mask_value=0.,
                                      input_shape=(timesteps, features)))
    model.add(tf.keras.layers.LSTM(32))

    output = model(inputs)
    # The time step 3 and 5 will be skipped from LSTM calculation.

See [the masking and padding guide](https://www.tensorflow.org/guide/keras/masking_and_padding)
for more details.