This layer wrapper allows to apply a layer to every temporal slice of an input
time_distributed(object, layer, ...)What to call the new Layer instance with. Typically a keras
Model, another Layer, or a tf.Tensor/KerasTensor. If object is
missing, the Layer instance is returned, otherwise, layer(object) is
returned.
a tf.keras.layers.Layer instance.
standard layer arguments.
Every input should be at least 3D, and the dimension of index one of the first input will be considered to be the temporal dimension.
Consider a batch of 32 video samples, where each sample is a 128x128 RGB image
with channels_last data format, across 10 timesteps.
The batch input shape is (32, 10, 128, 128, 3).
You can then use TimeDistributed to apply the same Conv2D layer to each
of the 10 timesteps, independently:
input <- layer_input(c(10, 128, 128, 3)) conv_layer <- layer_conv_2d(filters = 64, kernel_size = c(3, 3)) output <- input %>% time_distributed(conv_layer) output$shape # TensorShape([None, 10, 126, 126, 64])
Because TimeDistributed applies the same instance of Conv2D to each of the
timestamps, the same set of weights are used at each timestamp.
Other layer wrappers:
bidirectional()