time_distributed {keras} | R Documentation |
This layer wrapper allows to apply a layer to every temporal slice of an input
Description
This layer wrapper allows to apply a layer to every temporal slice of an input
Usage
time_distributed(object, layer, ...)
Arguments
object |
What to compose the new
|
layer |
a |
... |
standard layer arguments. |
Details
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.
See Also
Other layer wrappers:
bidirectional()