tf.keras.layers.Masking

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

Inherits From: Layer, Operation

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 = keras.models.Sequential()
model.add(keras.layers.Masking(mask_value=0.)
model.add(keras.layers.LSTM(32))
output = model(inputs)
# The time step 3 and 5 will be skipped from LSTM calculation.

Note: in the Keras masking convention, a masked timestep is denoted by a mask value of False, while a non-masked (i.e. usable) timestep is denoted by a mask value of True.

Methods

from_config

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@classmethod
from_config(
    config
)

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

symbolic_call

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symbolic_call(
    *args, **kwargs
)