This layer wrapper allows to apply a layer to every temporal slice of an input
time_distributed(object, layer, ...)
What to compose the new
Layer instance with. Typically a
Sequential model or a Tensor (e.g., as returned by
The return value depends on
Layer instance is returned.
Sequential model, the model with an additional layer is returned.
a Tensor, the output tensor from
layer_instance(object) is returned.
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
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])
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: