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tf.keras.layers.Masking

TensorFlow 2.0 version View source on GitHub

Class Masking

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

Inherits From: Layer

Aliases:

  • Class tf.compat.v1.keras.layers.Masking
  • Class tf.compat.v2.keras.layers.Masking

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:
model = Sequential()
model.add(Masking(mask_value=0., input_shape=(timesteps, features)))
model.add(LSTM(32))

__init__

View source

__init__(
    mask_value=0.0,
    **kwargs
)