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

In kerasR: R Interface to the Keras Deep Learning Library

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

Description

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.

Usage

Masking(mask_value, input_shape = NULL)

Arguments

mask_value	the value to use in the masking
input_shape	only need when first layer of a model; sets the input shape of the data

Author(s)

Taylor B. Arnold, [e-mail]

References

Chollet, Francois. 2015. Keras: Deep Learning library for Theano and TensorFlow.

See Also

Other layers: Activation, ActivityRegularization, AdvancedActivation, BatchNormalization, Conv, Dense, Dropout, Embedding, Flatten, GaussianNoise, LayerWrapper, LocallyConnected, MaxPooling, Permute, RNN, RepeatVector, Reshape, Sequential

kerasR documentation built on Aug. 17, 2022, 5:06 p.m.